Complementary noise cancellation and localization of vehicles

Abstract

The invention relates to an apparatus for an external system and / or an outer system, wherein the apparatus comprises, a noise cancellation module, wherein the noise cancellation module is configured to compensate for acoustic noise generated by the external and / or outer system; a discriminator module, wherein the discriminator module is configured to differentiate acoustic noise generated by at least one type of system from other acoustic noises; and a localization module, wherein the localization module is configured to differentiate acoustic noise generated by the external and / or outer system from at least one 3rd party system and / or locate the external and / or outer system and / or at least one 3rd party system according to at least one data, wherein the at least one data comprises at least one acoustic data, and wherein the at least one acoustic data comprises at least one acoustic noise data.

Description

[0001] Complementary Noise Cancellation and Localization of Vehicles Field

[0002] The invention lies in the field of noise cancellation and localization for vehicles. More particularly, the invention lies in the field of the symbiotic relationship between noise cancellation and localization for aerial, maritime, terrestrial and hybrid vehicles.

[0003] Background

[0004] In the development of unmanned aerial, maritime, and terrestrial vehicles, a lot more detailed requirements arise in the noise cancellation area.

[0005] Noise cancellation directed toward people outside of the vehicle is an emerging technology aimed at reducing the environmental noise impact of vehicles, especially in urban areas. This approach may involve using external speakers and active noise control (ANC) systems to cancel or minimize the noise generated by engines, exhaust systems, propellers... By deploying these noise-canceling techniques, engineers aim to enhance urban soundscapes, reduce noise pollution, and improve the overall quality of life for pedestrians and city dwellers.

[0006] Using a combination of microphones, sensors, and speakers mounted on the outside of vehicles. These systems detect the noise generated by the vehicle, such as motor noise, wind resistance. Quieter engines or sound-insulating materials, aren't enough, especially in the era of electric and hybrid vehicles. Although these vehicles have quieter engines, other noises like tire friction on asphalt and wind turbulence become more prominent. Noise-canceling systems, aimed at the surroundings, help mitigate these remaining noises, contributing to a quieter and more pleasant urban environment.

[0007] Development such as these have been put to use in the following inventions:

[0008] US 2018 / 0204561 Al discloses a rotating blade noise reduction device for reducing noise from a flight vehicle including rotating blades, the device includes loudspeakers, one or more reference microphones, an estimator, and a processor. The loudspeakers are arranged coaxially in a circumferential form for each of the rotating blades. The reference microphones acquire noise generated from the rotating blades and control sounds generated from the loudspeakers. The estimator estimates angular frequencies of the rotating blades. The processor generates control signals so as to reduce sound pressures at the reference microphones, delays the control signals by time delays corresponding to the loudspeakers dependent on installation angles between the loudspeakers arranged coaxially in a circumferential form from a circle center, the angular frequencies estimated, and a number of the loudspeakers, and inputs the control signals to the loudspeakers.

[0009] W02023223900A1 discloses an information processing device used to reduce noise generated by the operation of a rotor blade module including rotor blades and drive units that drive the rotor blades , wherein on the basis of noise data in which product information for the rotor blade module, the rotation speed of the rotor blades, and noise information for noise generated by the operation of the rotor blade module are associated with each other, product information acquired for one rotor blade module and noise information corresponding to the rotation speed are acquired, and a sound signal is generated for a sound that reduces noise based on the acquired noise information.

[0010] However, the counter-noise generated by the noise cancellation structure may also be used, in addition to the noise naturally generated by the vehicle, as a foundation for other purposes such as identification, localization and / or avoidance of objects / enhancing the localization capabilities of a system.

[0011] Summary

[0012] In light of the above, it is therefore an object of the present invention to overcome or at least to alleviate the shortcomings and disadvantages of the prior art. More particularly, it is an object of the present invention to take advantage of the noise cancellation structure and exploit it for localization purposes.

[0013] These objects are met by the present invention.

[0014] In a first aspect, the invention relates to an apparatus for an external system and / or an outer system, wherein the apparatus may comprise, a discriminator module, and a localization module. The apparatus may also comprise a noise cancellation module, an acquiring module, and / or a processing module and / or a collision avoidance module.

[0015] The external system may be a system manufactured without the apparatus and the outer system may be a system manufactured with the apparatus. The external system and / or the outer system may comprise at least one motor. The external system and / or the outer system may also comprise at least one of, but not limited to, a vehicle, an aircraft and / or an aircraft system, and / or the outer system may comprise an aircraft and / or an aircraft system, a watercraft and / or a watercraft system, and / or the outer system may comprise a watercraft and / or a watercraft system. The external system and / or the outer system may further comprise at least one of, but not limited to, a propeller and / or a propeller system, an airship, a ship, a drone, a vessel, a multicopter drone, and / or a submarine. A multicopter may be defined as an aerial vehicle whose motion is controlled by speeding or slowing multiple downward thrusting motor / propeller units.

[0016] Additionally and alternatively, the apparatus may be at least one of, but not limited to, a retrofit for the external system, a retrofit for an aircraft and / or an aircraft system, a retrofit for a watercraft and / or watercraft system, a retrofit for an airship, a retrofit for a ship, a retrofit for a drone, a retrofit for a vessel, a retrofit for a multicopter drone, and / or a retrofit for a submarine. A retrofit may be defined as a component or accessory added to something that did not have it when manufactured.

[0017] Furthermore, the apparatus may be a noise cancellation apparatus, a vehicle localization apparatus, wherein a vehicle may comprise a machine capable of movement, and / or an obstacle / object localization apparatus. Examples of obstacles may be but not limited to birds for aerial systems, fish for maritime systems, ... Obstacles may refer to static objects or objects in movement.

[0018] The apparatus may comprise one or more processing units configured to carry out computer instructions of a program (i.e. machine readable and executable instructions). The processing unit(s) may be singular or plural. For example, the data- processing system may comprise at least one of CPU, GPU, TPU, DSP, APU, ASIC, ASIP or FPGA. The data processing system may comprise memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and / or secondary memory (e.g. HDD, SDD). The data processing system may comprise volatile and / or non-volatile memory such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.

[0019] The apparatus's modules and / or at least one of the apparatus's modules may be configured for analog computing. The apparatus's modules and / or at least one of the apparatus's modules may also be configured for neuromorphic computing. The apparatus may comprise one or more analog and / or neuromorphic processing units configured to carry out computer instructions of a program (i.e. machine readable and executable instructions). The analog and / or neuromorphic processing unit(s) may be singular or plural. For example, the evaluation module may comprise at least one of but not limited to analog matrix processor(s), analog modular processor(s), reconfigurable analog modular processor(s), analog chips making use of but not limited to memristors, or neuromorphic chip(s).

[0020] Analog computing, is defined in this document as a type of computation where continuous physical phenomena, such as electrical voltage, mechanical motion, or fluid dynamics, are used to model and solve problems. Analog computing may refer to but is not limited to neuromorphic computing, defined as an approach to computing that mimics the structure and function of the human brain designing hardware and systems that replicate the neural networks found in biological brains. Analog and neuromorphic computing present better energy efficiency, faster processing and reduced complexity compared to regular computing. Neuromorphic computing may be especially advantageous in a sparce data environment. The implementation of analog and / or neuromorphic computing presents a preferred advantage of the current invention.

[0021] In one embodiment, the acquiring module may be configured to acquire at least one data and / or may be configured to acquire at least one data in real-time. The acquiring module may be also configured to acquire at least one of, but not limited to, at least one image data, at least one acoustic data, at least one wind data, at least one meteorological data, at least one atmospheric pressure data, at least one rotation rate data wherein the rotation rate data may comprise at least one data related to the rotation rate of the external and / or outer system, at least one acceleration data wherein the acceleration data may comprise at least one data related to the kinematic acceleration of the external and / or outer system, at least one voltage data wherein the voltage data relates to the voltage of the external system and / or outer system, at least one rotational speed data wherein the at least one rotational speed data relates to the at least one motor, and / or at least one humidity data.

[0022] In the case of using neuromorphic data, it should be obvious to a person skilled in the art that the data will be represented in a way that neuromorphic devices may interpret. For example, an image data would not comprise colored pixels like pixels comprised in an image captured by a regular camera, but would comprise the difference in movement captured by the neuromorphic camera, in that example, and represented in a grid corresponding to the resolution of the neuromorphic camera.

[0023] The acquiring module may further comprise at least one analog sensor, at least one digital sensor, and / or at least one neuromorphic sensor, wherein the at least one sensor may be configured to acquire the at least one data. The acquiring module may additionally be configured to output the at least one data to the processing module.

[0024] In another embodiment, the processing module may be configured to process at least one data into an at least one processed data, wherein the processed data may comprise at least one response data. The processing module may also be configured to convert the at least one data into at least one neuromorphic data wherein the at least one neuromorphic data may comprise the at least one data converted to be correctly read by a neuromorphic chip. The processing module may be further configured to output the at least one processed data to the noise cancellation module, the discriminator module, and / or the collision avoidance module.

[0025] In a further embodiment, the noise cancellation module may be configured to compensate for acoustic noise generated by the external and / or outer system. The noise cancellation module may also be configured to compensate for acoustic noise generated by the external and / or outer system according to at least one data and / or at least one processed data, wherein the at least one data may comprise at least one acoustic data. The compensation for acoustic noise may comprise reduction of acoustic noise. The acoustic noise may comprise noise generated by, but not limited to, at least one motor and / or a combination of propeller.

[0026] Furthermore, the noise cancellation module may be configured to perform acoustic noise cancellation, wherein active noise cancellation may comprise actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective may be may comprised outside of either the apparatus or the external and / or outer system. The noise cancellation module may also be configured to perform sectorial noise cancellation and / or sectorial active noise cancellation, wherein sectorial active noise cancellation may comprise actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective in may comprised outside of either the apparatus or the external and / or outer system wherein the outside perspective may be may comprised in a predetermined positional sector. The noise cancellation module may further be configured to reduce acoustic noise depicted in the at least one data by at least lOdB by making use of active noise cancellation and / or sectorial active noise cancellation. The noise cancellation module may additionally be configured to reduce the acoustic noise depicted in the at least one primary acoustic data by at least lOdB according to the outside perspective.

[0027] Moreover, the noise cancellation module may be configured to output at least one noise cancellation command, wherein the at least one noise cancellation command may be configured to compensate for acoustic noise generated by the external and / or outer system. The at least one noise cancellation command may be configured to be adjustable. The at least one noise cancellation command may also be configured to be adaptively adjustable according to the external and / or outer system. The at least one noise cancellation command may further be configured according to the content, the magnitude frequency response and / or phase response of the at least one acoustic data.

[0028] Additionally and alternatively, the noise cancellation module may be configured to filter out wind noise and / or aeroacoustic effects from the at least one acoustic data. The noise cancellation module may also be configured to compensate for acoustic noise generated by the external and / or outer system according to at least one acoustic data, wherein the at least one acoustic data may be configured to have been filtered to remove wind noise and / or aeroacoustic effects. Wind noise may be generated by air turbulence directly at the diaphragm of a microphone. The noise cancellation module may further be configured to generate at least one counter-noise signal. The noise cancellation module may be configured to generate at least one counter-noise signal according to the at least one data.

[0029] Furthermore, the noise cancellation module may be configured to generate the at least one counter-noise signal according to at least one feedforward counter-noise generating algorithm, at least one feedback counter-noise generating algorithm, and / or at least one hybrid counter-noise generating algorithm. The noise cancellation module may also be configured to adaptively generate at least one counter-noise signal, such that the adaptive generation may or may not depend of the at least one processed data. The noise cancellation module may further be configured to generate at least one counter-noise signal according to at least one Al algorithm. Feedforward counter-noise generating algorithm may comprise, but are not limited to, White Noise Generators, Pink Noise Generators, Brownian Noise Generators, Gaussian Noise Generators, Pseudorandom Noise Generators, Spectral Noise Generators, non-recursive FIR (finite impulse response) filter. Feedback counter-noise generating algorithm may comprise, but are not limited to, Adaptive Noise-Canceling Algorithms, Chaotic Signal Generators, Noise-Shaping Algorithms. Hybrid counter-noise generating algorithm, wherein hybrid may refer to a mix between feedforward and feedback algorithms, may comprise, but are not limited to Matched Noise Generators, Quantum Noise Generators.

[0030] Moreover, the noise cancelling module may be configured to predict acoustic noise to be generated by the external and / or outer system. The noise cancellation module may also be configured to perform acoustic noise cancellation according to the prediction of the acoustic noise generated by the external and / or outer system. The prediction may be generated by at least one Al algorithm.

[0031] The noise cancellation module may be configured to set up its parameters according to at least one Al algorithm. The noise cancellation module may also be configured to set up its parameters for the compensation of acoustic noise according to at least one Al algorithm. The parameters may comprise but are not limited to propeller / motor speeds, external and / or outer system motion parameters, control parameters. The at least one Al algorithm may be configured to implicitly learn the external and / or outer system 's response to extrinsic influences.

[0032] In one embodiment, the apparatus may comprise at least one actuator, wherein the at least one actuator may comprise at least one loudspeaker and / or at least one motor. The at least one external and / or outer system may also comprise the at least one motor. The noise cancelling module may also comprise at least one actuator. The at least one actuator may be configured to perform active noise cancellation and / or sectorial active noise cancellation. The at least one actuator may also be configured to perform active noise cancellation and / or sectorial active noise cancellation according to at least one noise cancellation command, and / or according to the outside perspective. The at least one actuator may further be configured to output at least one acoustic signal.

[0033] Furthermore, the acquiring module may be configured to detect at least one acoustic signal and / or at least one reflected acoustic signal outputted by the at least one actuator. The at least one actuator may also be configured to be calibrated according to the noise cancellation module, the localization module, and / or the collision avoidance module. This constitutes a preferred advantage of the present invention.

[0034] In another embodiment, the discriminator module may be configured to differentiate acoustic noise generated by at least one type of system from other acoustic noises. The discriminator module may also be configured to differentiate acoustic noise generated by at least one type of system from other acoustic noises according to at least one acoustic data, wherein the at least one type of system may comprise at least one of but not limited to, at least one motor, at least one vehicle, an aircraft and / or an aircraft system, a watercraft and / or a watercraft system, at least one propeller and / or a propeller system, an airship, a ship, a drone, a vessel, a multicopter drone, and / or a submarine. The acoustic noise may include wind noise, wherein wind noise may be generated by air turbulence directly at the diaphragm of a microphone.

[0035] Furthermore, the discriminator module may be configured to output at least one primary acoustic data wherein the at least one primary acoustic data relates to at least one acoustic noise generated by the at least one type of system. The discriminator module may also be configured to output at least one secondary acoustic data, wherein the at least one secondary acoustic data relates to other acoustic noises, wherein other acoustic noises relate to at least one acoustic noise not generated by the at least one type of system. The discriminator module may further be configured to output the at least one primary acoustic data to the localization module. The discriminator module may also be configured to output the at least one secondary acoustic data to the collision avoidance module.

[0036] In one embodiment, the localization module may be configured to differentiate acoustic noise generated by the external and / or outer system from at least one 3rdparty system. The localization module may also be configured to locate the external and / or outer system and / or at least one 3rdparty system. The localization module may further be configured to locate the external and / or outer system and / or at least one 3rdparty system with respect to each other, with respect to at least one data (relative to the at least one data), and / or according to at least one data (may be not relative to the at least one data). The at least one data may comprise at least one data related to at least one data acquired by the acquiring module. The at least one data may further comprise at least one of, but not limited to at least one map data, at least one GPS data, at least one IMU data, at least one triangulation result according to at least one weather data and GPS data. The at least one map data may comprise but is not limited to at least one landmark, and / or at least one 3D point / scan. The at least one IMU / GPS data may be considered as a point of origin based on a map data for the localization result outputted by the localization module. The point of origin may be updated with respect to the output of the localization module. This constitutes a preferred advantage of the present invention as the present invention may increase the reliability of an IMU data point without necessarily relying on GPS data.

[0037] Furthermore, the localization module may be configured to differentiate acoustic noise generated by the external and / or outer system from at least one 3rdparty system according to at least one data, wherein the at least one 3rdparty system may comprise the at least one type of system as defined previously. The at least one data may comprise at least one acoustic data, wherein the at least one acoustic data may comprise at least one acoustic noise data. The at least one data may also comprise the at least one primary acoustic data.

[0038] Moreover, the localization module may be configured to output at least one 3rdparty system acoustic data wherein the at least one 3rdparty system acoustic data relates to acoustic noise generated by the at least one 3rdparty system. The localization module may also be configured to output at least one self-system acoustic data wherein the at least one self-system acoustic data relates to acoustic noise generated by the external system and / or the outer system.

[0039] Additionally and alternatively, the localization module may be configured to transmit and / or receive at least one data to and / or from the collision avoidance module. The localization module may also be configured to output at least one data to the external and / or outer system, and / or to an external device / server / service independent of the apparatus. The localization module may further be configured to output at least one location data to the noise cancellation module, wherein the at last one location data relates to the outside perspective and / or receive the at least one data from the collision avoidance module. The localization module may additionally be configured to improve the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm. These algorithms may be configured to check for implausible jumps or unphysical behavior in order to improve an estimation of the location and also improve the differentiation between primary signals and reflected signals. These algorithms may also be configured to invalidate and / or validate at least one location data generated by the localization module.

[0040] The localization module may be configured to generate at least one localization data, wherein the localization module is configured to implement at least one of, but not limited to, TDOA (time difference of arrival), beamforming, MUSIC (Multiple Signal Classification) or other approaches. The localization module may also be configured to augment the accuracy of the localization data by making use of a change-point or break-point or outlier detection algorithm.

[0041] In another embodiment, the localization module may comprise a 3rdparty localization module, wherein the 3rdparty localization module may be configured to locate at least one 3rdparty system wherein the at least one 3rdparty system does not may comprise the external system and / or outer system. The 3rdparty localization module may also be configured to locate at least one 3rdparty system according to the at least one 3rdparty system acoustic data. The 3rdparty localization module may be further configured to output at least one operation command to the at least one actuator, wherein the at least one operational command may comprise at least one command configured to output at least one acoustic signal.

[0042] Furthermore, the 3rdparty localization module may be configured to detect at least one 3rdparty system according to the at least one acoustic signal and / or at least one reflected acoustic signal. The 3rdparty localization module may also be configured to differentiate 3rdparty direct noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty direct noise data may comprise at least one non-reflected acoustic noise generated by at least one 3rdparty system. The 3rdparty localization module may further be configured to differentiate 3rdparty reflected noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty reflected noise data may comprise at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the at least one 3rdparty system.

[0043] The 3rdparty localization module may be configured to differentiate the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to metadata related to at least one data, wherein the at least one data may result in the at least one 3rdparty system acoustic data, wherein the at least one data may have been detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system. The 3rdparty localization module may also be configured to extract the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the angle the at least one data resulting in the at least one 3rdparty system acoustic data may have been detected at, the magnitude of the at least one data resulting in the at least one 3rdparty system acoustic data, and / or the time of arrival the at least one data resulting in the at least one 3rdparty system acoustic data may have been detected at. The 3rdparty localization module may further be configured to extract the 3rdparty direct noise data and / or the 3rdparty reflected noise data by making use of at least one Al algorithm, such as but not limited to deep learning methods. Non-AI algorithm may comprise but not be limited to correlation algorithms, matching pursuit, orthogonal matching pursuit, basis-pursuit denoising. The 3rdparty localization module may additionally be configured to identify the location of the at least one 3rdparty system according to at least one map / GPS / IMU data and at least one self-reflected noise data. The 3rdparty localization module may also be configured to identify the location of the at least one 3rdparty system according to at least one map / GPS / IMU data and at least one self-reflected noise data by making use of at least one data-driven Al algorithm.

[0044] In a further embodiment, the localization module may comprise a self-localization module, wherein the self-localization module may be configured to locate the external system and / or the outer system. The self-localization module may also be configured to locate the external system and / or the outer system according to the at least one self-system acoustic data. The self-localization module may further be configured to output at least one operation command to the at least one actuator, wherein the at least one operational command may comprise at least one command configured to output at least one acoustic signal.

[0045] Furthermore, the self-localization module may be configured to detect the external and / or outer system according to the at least one acoustic signal and / or at least one reflected acoustic signal. The self-localization module may also be configured to extract self-direct noise data from the at least one self-system acoustic data, wherein self-direct noise data may comprise at least one non-reflected acoustic noise generated by the external and / or outer system. The self-localization module may further be configured to extract selfreflected noise data from the at least one self-system acoustic data, wherein self-reflected noise data may comprise at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the external and / or outer system.

[0046] The self-localization module may be configured to extract the self-direct noise data and / or the self-reflected noise data according to metadata related to at least one data, wherein the at least one data may result in the at least one self-system acoustic data, wherein the at least one data may have been detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system. The self-localization module may also be configured to extract the self-direct noise data and / or the self-reflected noise data according to, but not limited to, the angle the at least one data resulting in the at least one self-system acoustic data may have been detected at, the magnitude of the at least one data resulting in the at least one self-system acoustic data, and / or the time of arrival the at least one data resulting in the at least one self-system acoustic data may have been detected at. The self-localization module may further be configured to extract the self-direct noise data and / or the self-reflected noise data by making use of at least one Al algorithm, such as but not limited to deep learning methods. Non-AI algorithm may comprise but not be limited to correlation algorithms, matching pursuit, orthogonal matching pursuit, basis-pursuit denoising. The self-localization module may additionally be configured to identify the location of the external and / or outer system according to at least one map / GPS / IMU data and at least one 3rdparty reflected noise data. The self-localization module may also be configured to identify the location of the external and / or outer system according to at least one map / GPS / IMU data and at least one 3rdparty reflected noise data by making use of at least one data-driven Al algorithm.

[0047] Data sources that may be related to the primary and / or reflected noise such as wave direction height and speed, speed of the carrier vehicle of the system, weather data (in particular wind) and other data sources that may be affecting reflection surfaces may be used by the apparatus to more accurately differentiate primary signals from reflected signals as well as self-locate more accurately.

[0048] The apparatus may be configured to integrate additional information sources and / or sensor signals into audio processing in order to enhance localization (3rdparty localization and / or self-localization) and differentiation between primary and reflected signals. An example of the integration may be, but is not limited to, water wave motion on sea influences detection of drones (especially swimming drones) etc. The apparatus may be configured to utilize at least one sensor fusion approach such as but not limited to Particle Filter, Kalman Filter and / or Bayesian Networks. The apparatus may also be configured to utilize multimodal transformer models to integrate multiple sensor signals.

[0049] In one embodiment, the collision avoidance module may be configured to output at least one navigation command to the external system and / or the outer system. The collision avoidance module may also be configured to implement at least one collision avoidance algorithm based on at least one data, wherein the at least one data may comprise at least one data related to at least one data acquired by the acquiring module. The collision avoidance module may further be configured to implement at least one collision avoidance algorithm based on at least one acoustic data.

[0050] Furthermore, the collision avoidance module may comprise an object detection module, wherein the at least one object detection module may be configured to detect at least one obstacle. The at least one object detection module may also be configured to detect at least one obstacle according to at least one data, wherein the at least one data may comprise at least one data related to at least one data acquired by the acquiring module. The at least one object detection module may further be configured to output at least one operation command to the at least one actuator, wherein the at least one operational command may comprise at least one command to output at least one acoustic signal. The at least one data may further comprise at least one of, but not limited to at least one map data, at least one GPS data, at least one IMU data, at least one triangulation result according to at least one weather data and GPS data. The at least one map data may comprise but is not limited to at least one landmark, and / or at least one 3D point / scan. The at least one IMU / GPS data may be considered as a point of origin based on a map data for the object detection result outputted by the object detection module. The point of origin may be updated with respect to the output of the object detection module. This constitutes a preferred advantage of the present invention as the present invention may increase the reliability of an IMU data point without necessarily relying on GPS data.

[0051] Moreover, the at least one object detection module may be configured to detect at least one obstacle according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0052] The object detection module may also be configured to improve the accuracy of the location of the obstacle by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0053] These algorithms may be configured to check for implausible jumps or unphysical behavior in order to improve an estimation of the location and also improve the differentiation between primary signals and reflected signals. These algorithms may also be configured to invalidate and / or validate at least one object detection data generated by the object detection module.

[0054] The object detection module may be configured to generate at least one object detection data, wherein the object detection module is configured to implement at least one of, but not limited to, TDOA (time difference of arrival), beamforming, MUSIC (Multiple Signal Classification) or other approaches. The object detection module may also be configured to augment the accuracy of the localization data by making use of a change-point or break- point or outlier detection algorithm.

[0055] The object detection module may further be configured to extract obstacle-direct noise data from the at least one secondary acoustic data, wherein obstacle-direct noise data may comprise at least one non-reflected acoustic noise generated by at least one obstacle / object. The object detection module may additionally be configured to extract obstacle-reflected noise data from the at least one secondary acoustic data, wherein obstacle-reflected noise data may comprise at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by at least one obstacle / object.

[0056] The object detection module may be configured to extract the obstacle-direct noise data and / or the obstacle-reflected noise data according to metadata related to at least one data, wherein the at least one data may result in at least part of the at least one secondary acoustic data, wherein the at least one data may have been detected with respect to the position of the at least one sensor and / or the position of the obstacle / object. The object detection module may also be configured to extract the obstacle-direct noise data and / or the obstacle -reflected noise data according to, but not limited to, the angle the at least one data resulting in in at least part of the at least one secondary acoustic data, the magnitude of the at least one data resulting in at least part of the at least one secondary acoustic data, and / or the time of arrival the at least one data resulting in at least part of the at least one secondary acoustic data. The object detection module may further be configured to extract the obstacle-direct noise data and / or the obstacle-reflected noise data by making use of at least one Al algorithm, such as but not limited to deep learning methods. Non-AI algorithm may comprise but not be limited to correlation algorithms, matching pursuit, orthogonal matching pursuit, basis-pursuit denoising. The object detection module may additionally be configured to identify the location of the at least one obstacle according to at least one map / GPS / IMU data and at least one self-reflected noise data. The object detection module may also be configured to identify the location of the at least one obstacle according to at least one map / GPS / IMU data and at least one selfreflected noise data by making use of at least one data-driven Al algorithm.

[0057] Additionally and alternatively, the object detection module may comprise an object movement prediction module, wherein the object movement prediction module may be configured to predict at least one movement of at least one object. The object movement prediction may be configured to utilize at least one of, but not limited to Kalman filters and polynomial fitting, and / or Optical flow. The object movement prediction module may also be configured to predict at least one movement of at least one object according to at least one Al algorithm, such as but not limited to particle filter, RNN (recurrent neural network), GPR (gaussian process regression), MDP (Markov decision process), GAN, LSTM or more particularly a variant of social-LSTM for a lot of objects, transformer models, multimodal transformer models for sensor fusion.

[0058] In another embodiment, the collision avoidance module may comprise an area monitoring module, wherein the area monitoring module may be configured to map at least one area according to at least one data. The area monitoring module may also be configured to map at least one area according to at least one data, wherein the at least one data may comprise at least one data related to at least one data acquired by the acquiring module. The at least one data may also comprise at least one image data. The area monitoring module may comprise at least one Al module, wherein the at least one Al module may be configured as a neuromorphic neural network. The at least one Al module may further be configured to map at least one area according to the at least one image data. The at least one Al-module may also be configured to implement at least one Tiny Object Detection algorithm. The area monitoring module may further be configured to map at least one area according to at least one acoustic data. The area monitoring module may additionally be configured to map at least one area according to at least one SLAM (Simultaneous Localization and Mapping) algorithm.

[0059] In a further embodiment, the collision avoidance module may comprise a navigation module, wherein the navigation module may be configured to output at least one navigation command to the external and / or outer system.

[0060] Additionally and alternatively, the modules of the apparatus may be configured to operate separately. The apparatus may also be configured to implement at least one vibration suppression technique to suppress the vibrations caused by the at least one motor. The modules of the apparatus may further be configured to operate independently of their location with respect to the external and / or outer system. For examples, some modules may be located on a server. The modules of the apparatus may additionally be configured to transmit and / or receive at least one data.

[0061] Furthermore, the apparatus may comprise a noise reflection detection module, wherein the noise reflection detection module may be configured to perform the at least one function related to at least one reflected data described in the description of the present invention. The apparatus may be configured to match at least one direct / original signal to at least one reflected signal according to at least one cross-correlation algorithm, such that the at least one cross-correlation algorithm is configured to measure similarity between data. The apparatus may also be configured to match at least one direct / original signal to at least one reflected signal according to at least one matched filter, such that the at least one matched filter is configured to detect known signals in noisy environments. The apparatus may further be configured to detect at least one direct / original signal and / or at least one reflected signal according to at least one blind source separation algorithm, such as but not limited to ICA (Independent Component Analysis), and / or PCA (Principal Component Analysis).

[0062] The apparatus may be configured to differentiate between at least one direct / original signal and / or at least one reflected signal according to at least one time frequency analysis algorithm, such as but not limited to STFT (Short-Time Fourier Transform). The apparatus may also be configured to differentiate between at least one direct / original signal and / or at least one reflected signal according to at least one, but not limited to, matching pursuit algorithm and / or Orthogonal matching pursuit and / or Basis-pursuit.

[0063] Moreover, the apparatus may be configured to differentiate between at least one direct / original signal and / or at least one refracted signal. The apparatus may be configured to differentiate between at least one direct / original signal and / or at least one refracted signal by making use of the apparatus as described in the description of the present invention replacing the reflected signal with a refracted signal. The apparatus may also be configured to improve the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm. These algorithms may be configured to check for implausible jumps or unphysical behavior in order to improve an estimation of the data and also improve the differentiation between direct signals and refracted signals. These algorithms may also be configured to invalidate and / or validate at least one object detection data generated by the apparatus.

[0064] The apparatus may also be configured to identify the medium in which the external and / or outer system is present an / or the medium in which the external and / or outer system is predicted to enter. The apparatus may further be configured to identify the medium in which the external and / or outer system is present according to at least one data, wherein the at least one data may comprise at least one reflected and / or refracted data. Examples of the medium may comprise but is not limited to the seabed under water, streets or soil or water under the external and / or outer system, the medium's composition, texture, consistency, properties and conditions.

[0065] Additionally, the discriminator module may be configured to store acoustic noise generated by at least one type of system and / or at least one obstacle. The at least one actuator may also be configured to output at least one acoustic signal, wherein the at least one acoustic signal may correspond to at least one acoustic noise generated by at least one type of system and / or at least one obstacle, wherein the at least one type of system may correspond to at least one of any type of system mentioned previously herein. The system may thus serve the double purpose of noise cancellation and camouflaging its own generated noise as another type of system independently of parameters that may affect the acoustic noise generated such as but not limited to the size of the system and / or components. The system may further be configured to modify the at least one acoustic signal outputted by the at least one actuator by for example modifying its frequency.

[0066] In a second aspect, the invention relates to a method for an external system and / or an outer system, wherein the method may comprise, operating a discriminator module, and operating a localization module. The method may also comprise operating a noise cancellation module and / or an acquiring module and / or a processing module, and / or a collision avoidance module.

[0067] The external system may be a system manufactured without the modules operated by the method and the outer system may be a system manufactured with the modules operated by the method. The external system and / or the outer system may comprise at least one motor. The external system and / or the outer system may also comprise at least one of but not limited to, a vehicle, an aircraft and / or an aircraft system, and / or the outer system may comprise an aircraft and / or an aircraft system, a watercraft and / or a watercraft system, and / or the outer system may comprise a watercraft and / or a watercraft system. The external system and / or the outer system may further comprise at least one of, but not limited to, a propeller and / or a propeller system, an airship, a ship, a drone, a vessel, a multicopter drone, and / or a submarine. A multicopter may be defined as an aerial vehicle whose motion is controlled by speeding or slowing multiple downward thrusting motor / propeller units.

[0068] Additionally and alternatively, the method may comprise at least one of, but not limited to, operating a retrofit for the external system, operating a retrofit for an aircraft and / or an aircraft system, operating a retrofit for a watercraft and / or watercraft system, operating a retrofit for an airship, operating a retrofit for a ship, operating a retrofit for a drone, operating a retrofit for a vessel, operating a retrofit for a multicopter drone, and / or operating a retrofit for a submarine. A retrofit may be defined as a component or accessory added to something that did not have it when manufactured.

[0069] Furthermore, the method may be a noise cancellation method, a vehicle localization method, wherein a vehicle may comprise a machine capable of movement and / or an obstacle / object localization method. Examples of obstacles may be but not limited to birds for aerial systems, fish for maritime systems, ... Obstacles may refer to static objects or objects in movement. The method's modules and / or at least one of the method's modules may be configured for analog computing. The method's modules and / or at least one of the method's modules may be configured for neuromorphic computing.

[0070] Analog computing, is defined in this document as a type of computation where continuous physical phenomena, such as electrical voltage, mechanical motion, or fluid dynamics, are used to model and solve problems. Analog computing may refer to but is not limited to neuromorphic computing, defined as an approach to computing that mimics the structure and function of the human brain designing hardware and systems that replicate the neural networks found in biological brains. Analog and neuromorphic computing present better energy efficiency, faster processing and reduced complexity compared to regular computing. Neuromorphic computing may be especially advantageous in a sparce data environment. The implementation of analog and / or neuromorphic computing presents a preferred advantage of the current invention.

[0071] In one embodiment, operating the acquiring module may comprise acquiring at least one data and / or may comprise acquiring at least one data in real-time. Operating the acquiring module may also comprise acquiring at least one of but not limited to, at least one image data, at least one acoustic data, at least one wind data, at least one meteorological data, at least one atmospheric pressure data, at least one rotation rate data wherein the rotation rate data may comprise at least one data related to the rotation rate of the external and / or outer system, at least one acceleration data wherein the acceleration data may comprise at least one data related to the kinematic acceleration of the external and / or outer system, at least one voltage data wherein the voltage data relates to the voltage of the external system and / or outer system, at least one rotational speed data wherein the at least one rotational speed data relates to the at least one motor, and / or at least one humidity data.

[0072] In the case of using neuromorphic data, it should be obvious to a person skilled in the art that the data will be represented in a way that neuromorphic devices may interpret. For example, an image data would not comprise colored pixels like pixels comprised in an image captured by a regular camera, but would comprise the difference in movement captured by the neuromorphic camera, in that example, and represented in a grid corresponding to the resolution of the neuromorphic camera.

[0073] Operating the acquiring module may further comprise operating at least one analog sensor, operating at least one digital sensor and / or operating at least one neuromorphic sensor, wherein operating the at least one sensor may comprise acquiring the at least one data. Operating the acquiring module may additionally comprise outputting the at least one data to the processing module.

[0074] In another embodiment, operating the processing module may comprise processing at least one data into an at least one processed data, wherein the processed data may comprise at least one response data. Operating the processing module may also comprise converting the at least one data into at least one neuromorphic data wherein the at least one neuromorphic data may comprise the at least one data converted to be correctly read by a neuromorphic chip. Operating the processing module may further comprise outputting the at least one processed data to the noise cancellation module, the discriminator module and / or the collision avoidance module.

[0075] In a further embodiment, operating the noise cancellation module may comprise compensating for acoustic noise generated by the external and / or outer system. Operating the noise cancellation module may comprise compensating for acoustic noise generated by the external and / or outer system according to at least one data and / or at least one processed data, wherein the at least one data may comprise at least one acoustic data. The compensation for acoustic noise may comprise reduction of acoustic noise. The acoustic noise may comprise noise generated by, but not limited to, at least one motor and / or a combination of propeller.

[0076] Furthermore, operating the noise cancellation module may comprise performing acoustic noise cancellation, wherein active noise cancellation may comprise actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective may be may comprised outside of either the method or the external and / or outer system. Operating the noise cancellation module may also comprise performing sectorial noise cancellation and / or sectorial active noise cancellation, wherein sectorial active noise cancellation may comprise actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective in may comprised outside of either the method or the external and / or outer system wherein operating the outside perspective may be may comprised in a predetermined positional sector. Operating the noise cancellation module may further comprise reducing acoustic noise depicted in the at least one data by at least lOdB by making use of active noise cancellation and / or sectorial active noise cancellation. Operating the noise cancellation module may additionally, comprise reducing the acoustic noise depicted in the at least one primary acoustic data by at least lOdB according to the outside perspective.

[0077] Moreover, operating the noise cancellation module may comprise outputting at least one noise cancellation command, wherein operating the at least one noise cancellation command may comprise compensating for acoustic noise generated by the external and / or outer system. The at least one noise cancellation command may be adjustable and / or adaptively adjustable according to the external and / or outer system. The at least one noise cancellation command may further be configured according to the content, the magnitude frequency response and / or phase response of the at least one acoustic data.

[0078] Additionally and alternatively, wherein operating the noise cancellation module may comprise filtering out wind noise and / or aeroacoustic effects from the at least one acoustic data. Operating the noise cancellation module may also comprise compensating for acoustic noise generated by the external and / or outer system according to at least one acoustic data, wherein the at least one acoustic data has been filtered out to remove wind noise and / or aeroacoustic effects. Wind noise may be generated by air turbulence directly at the diaphragm of a microphone. Operating the noise cancellation module may further comprise generating at least one counter-noise signal. Operating the noise cancellation module may comprise generating at least one counter-noise signal according to the at least one data.

[0079] Furthermore, operating the noise cancellation module may comprise generating the at least one counter-noise signal according to at least one feedforward counter-noise generating algorithm, at least one feedback counter-noise generating algorithm, and / or at least one hybrid counter-noise generating algorithm. Operating the noise cancellation module may also comprise adaptively generating at least one counter-noise signal, such that the adaptive generation may or may not depend of the at least one processed data. Operating the noise cancellation module may further comprise generating at least one counter-noise signal according to at least one Al algorithm. Feedforward counter-noise generating algorithm may comprise, but are not limited to, White Noise Generators, Pink Noise Generators, Brownian Noise Generators, Gaussian Noise Generators, Pseudorandom Noise Generators, Spectral Noise Generators, non-recursive FIR (finite impulse response) filter. Feedback counter-noise generating algorithm may comprise, but are not limited to, Adaptive Noise-Canceling Algorithms, Chaotic Signal Generators, Noise-Shaping Algorithms. Hybrid counter-noise generating algorithm, wherein hybrid may refer to a mix between feedforward and feedback algorithms, may comprise, but are not limited to Matched Noise Generators, Quantum Noise Generators.

[0080] Moreover, operating the noise cancelling module may comprise predicting acoustic noise to be generated by the external and / or outer system. Operating the noise cancellation module may comprise performing acoustic noise cancellation according to the prediction of the acoustic noise generated by the external and / or outer system. The prediction may be generated by at least one Al algorithm. Operating the noise cancellation module may comprise setting up its parameters according to at least one Al algorithm. Operating the noise cancellation module may also comprise setting up its parameters for the compensation of acoustic noise according to at least one Al algorithm. The parameters may comprise but are not limited to propeller / motor speeds, external and / or outer system motion parameters, control parameters. The at least one Al algorithm may comprise implicitly learning the external and / or outer system 's response to extrinsic influences.

[0081] In one embodiment, the method may comprise operating at least one actuator, wherein operating the at least one actuator may comprise operating at least one loudspeaker, and / or at least one motor. Operating the at least one external and / or outer system may also comprise operating the at least one motor. Operating the noise cancelling module may also comprise operating at least one actuator. Operating the at least one actuator may comprise performing active noise cancellation and / or sectorial active noise cancellation. Operating the at least one actuator may also comprise performing active noise cancellation and / or sectorial active noise cancellation according to at least one noise cancellation command and / or according to the outside perspective. Operating the at least one actuator may further comprise outputting at least one acoustic signal.

[0082] Furthermore, operating the acquiring module may comprise detecting at least one acoustic signal and / or at least one reflected acoustic signal outputted by the at least one actuator. Operating the at least one actuator may comprise calibrating the at least one actuator according to the noise cancellation module, the localization module, and / or the collision avoidance module. This constitutes a preferred advantage of the present invention.

[0083] In another embodiment, operating the discriminator module may comprise differentiating acoustic noise generated by at least one type of system from other acoustic noises. Operating the discriminator module may also comprise differentiating acoustic noise generated by at least one type of system from other acoustic noises according to at least one acoustic data, wherein the at least one type of system may comprise at least one of but not limited to, at least one motor, at least one vehicle, an aircraft and / or an aircraft system, a watercraft and / or a watercraft system, at least one propeller and / or a propeller system, an airship, a ship, a drone, a vessel, a multicopter drone, and / or a submarine. The acoustic noise may include wind noise, wherein wind noise may be generated by air turbulence directly at the diaphragm of a microphone.

[0084] Furthermore, operating the discriminator module may comprise outputting at least one primary acoustic data wherein the at least one primary acoustic data relates to at least one acoustic noise generated by the at least one type of system. Operating the discriminator module may also comprise outputting at least one secondary acoustic data, wherein the at least one secondary acoustic data relates to other acoustic noises, wherein other acoustic noises relate to at least one acoustic noise not generated by the at least one type of system. Operating the discriminator module may further comprise outputting the at least one primary acoustic data to the localization module. Operating the discriminator module may also comprise outputting the at least one secondary acoustic data to the collision avoidance module.

[0085] In one embodiment, operating the localization module may comprise differentiating acoustic noise generated by the external and / or outer system from at least one 3rdparty system. Operating the localization module may also comprise locating the external and / or outer system and / or at least one 3rdparty system. Operating the localization module may further comprise locating the external and / or outer system and / or at least one 3rdparty system with respect to each other, with respect to at least one data (relative to the at least one data), and / or according to at least one data (may be not relative to the at least one data). The at least one data may comprise at least one data related to at least one data acquired by operating the acquiring module. The at least one data may further comprise at least one of, but not limited to at least one map data, at least one GPS data, at least one IMU data, at least one triangulation result according to at least one weather data and GPS data. The at least one map data may comprise but is not limited to at least one landmark, and / or at least one 3D point / scan. The at least one IMU / GPS data may be considered as a point of origin based on a map data for the localization result outputted by the localization module. The point of origin may be updated with respect to the output of the operation of the localization module. This constitutes a preferred advantage of the present invention as the present invention may increase the reliability of an IMU data point without necessarily relying on GPS data.

[0086] Furthermore, operating the localization module may comprise differentiating acoustic noise generated by the external and / or outer system from at least one 3rdparty system according to at least one data, wherein the at least one 3rdparty system may comprise the at least one type of system as defined previously. The at least one data may comprise at least one acoustic data, wherein the at least one acoustic data may comprise at least one acoustic noise data. The at least one data may also comprise the at least one primary acoustic data.

[0087] Moreover, operating the localization module may comprise outputting at least one 3rdparty system acoustic data wherein the at least one 3rdparty system acoustic data relates to acoustic noise generated by the at least one 3rdparty system. Operating the localization module may also comprise outputting at least one self-system acoustic data wherein the at least one self-system acoustic data relates to acoustic noise generated by the external system and / or the outer system.

[0088] Additionally and alternatively, operating the localization module may comprise transmitting and / or receiving at least one data to and / or from the collision avoidance module. Operating the localization module may also comprise outputting at least one data to the external and / or outer system, and / or to an external device / server / service independent of the method. Operating the localization module may further comprise outputting at least one location data to the noise cancellation module, wherein operating the at last one location data may relate to the outside perspective and / or receiving the at least one data from the collision avoidance module. Operating the localization module may additionally comprise improving the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm. These algorithms may comprise checking for implausible jumps or unphysical behavior in order to improve an estimation of the location and also improve the differentiation between primary signals and reflected signals. These algorithms may also comprise invalidating and / or validating at least one location data generated by the localization module.

[0089] Operating the localization module may comprise generating at least one localization data, wherein operating the localization module may comprise implementing at least one of, but not limited to, TDOA (time difference of arrival), beamforming, MUSIC (Multiple Signal Classification) or other approaches. Operating the localization module may also comprise augmenting the accuracy of the localization data by making use of a change-point or breakpoint or outlier detection algorithm.

[0090] In another embodiment, operating the localization module may comprise operating a 3rdparty localization module, wherein operating the 3rdparty localization module may comprise locating at least one 3rdparty system wherein the at least one 3rdparty system does not may comprise the external system and / or outer system. Operating the 3rdparty localization module may comprise locating at least one 3rdparty system according to the at least one 3rdparty system acoustic data. Operating the 3rdparty localization module may further comprise outputting at least one operation command to the at least one actuator, wherein the at least one operational command may comprise at least one command configured to output at least one acoustic signal.

[0091] Furthermore, operating the 3rdparty localization module may comprise detecting at least one 3rdparty system according to the at least one acoustic signal and / or at least one reflected acoustic signal. Operating the 3rdparty localization module may also comprise differentiating 3rdparty direct noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty direct noise data may comprise at least one non-reflected acoustic noise generated by at least one 3rdparty system. Operating the 3rdparty localization module may further comprise differentiating 3rdparty reflected noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty reflected noise data may comprise at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the at least one 3rdparty system.

[0092] Operating the 3rdparty localization module may comprise differentiating the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to metadata related to at least one data, wherein the at least one data may result in the at least one 3rdparty system acoustic data, wherein the at least one data may have been detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system. Operating the 3rdparty localization module may also comprise extracting the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to, but not limited to, the angle the at least one data resulting in the at least one 3rdparty system acoustic data may have been detected at, the magnitude of the at least one data resulting in the at least one 3rdparty system acoustic data, and / or the time of arrival the at least one data resulting in the at least one 3rdparty system acoustic data may have been detected at. Operating the 3rdparty localization module may further comprise extracting the 3rdparty direct noise data and / or the 3rdparty reflected noise data by making use of at least one Al algorithm, such as but not limited to deep learning methods. Non-AI algorithm may comprise but not be limited to correlation algorithms, matching pursuit, orthogonal matching pursuit, basispursuit denoising. Operating the 3rdparty localization module may additionally comprise identifying the location of the at least one 3rdparty system according to at least one map / GPS / IMU data and at least one self- reflected noise data. Operating the 3rdparty localization module may also comprise identifying the location of the at least one 3rdparty system according to at least one map / GPS / IMU data and at least one self-reflected noise data by making use of at least one data-driven Al algorithm.

[0093] In a further embodiment, operating the localization module may comprise operating a selflocalization module, wherein operating the self-localization module may comprise locating the external system and / or the outer system. Operating the self-localization module may also comprise locating the external system and / or the outer system according to the at least one self-system acoustic data. Operating the self-localization module may further comprise outputting at least one operation command to the at least one actuator, wherein the at least one operational command may comprise at least one command configured to output at least one acoustic signal. Furthermore, operating the self-localization module may comprise detecting the external and / or outer system according to the at least one acoustic signal and / or at least one reflected acoustic signal. Operating the self-localization module may also comprise extracting self-direct noise data from the at least one self-system acoustic data, wherein self-direct noise data may comprise at least one non-reflected acoustic noise generated by the external and / or outer system. Operating the self-localization module may further comprise extracting self- reflected noise data from the at least one self-system acoustic data, wherein self- reflected noise data may comprise at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the external and / or outer system.

[0094] Operating the self-localization module may comprise extracting the self-direct noise data and / or the self-reflected noise data according to metadata related to at least one data, wherein the at least one data may result in the at least one self-system acoustic data, wherein the at least one data may have been detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system. Operating the self-localization module may also comprise extracting the self-direct noise data and / or the self-reflected noise data according to, but not limited to, the angle the at least one data resulting in the at least one self-system acoustic data may have been detected at, the magnitude of the at least one data resulting in the at least one self-system acoustic data, and / or the time of arrival the at least one data resulting in the at least one self-system acoustic data may have been detected at. Operating the self-localization module may further comprise extracting the self-direct noise data and / or the self-reflected noise data by making use of at least one Al algorithm, such as but not limited to deep learning methods. Non-AI algorithm may comprise but not be limited to correlation algorithms, matching pursuit, orthogonal matching pursuit, basis-pursuit denoising. Operating the selflocalization module may additionally comprise identifying the location of the external and / or outer system according to at least one map / GPS / IMU data and at least one 3rdparty reflected noise data. Operating the self-localization module may also comprise identifying the location of the external and / or outer system according to at least one map / GPS / IMU data and at least one 3rdparty reflected noise data by making use of at least one data- driven Al algorithm.

[0095] Data sources that may be related to the primary and / or reflected noise such as wave direction height and speed, speed of the carrier vehicle of the system, weather data (in particular wind) and other data sources that may be affecting reflection surfaces may be used by the apparatus to more accurately differentiate primary signals from reflected signals as well as self-locate more accurately. The method may comprise integrating additional information sources and / or sensor signals into audio processing in order to enhance localization (3rdparty localization and / or selflocalization) and differentiation between primary and reflected signals. An example of the integration may be, but is not limited to, water wave motion on sea influences detection of drones (especially swimming drones) etc. The method may comprise utilizing at least one sensor fusion approach such as but not limited to Particle Filter, Kalman Filter and / or Bayesian Networks. The apparatus may also be configured to utilize multimodal transformer models to integrate multiple sensor signals.

[0096] In one embodiment, operating the collision avoidance module may comprise outputting at least one navigation command to the external system and / or the outer system. Operating the collision avoidance module may also comprise implementing at least one collision avoidance algorithm based on at least one data, wherein the at least one data may comprise at least one data related to at least one data acquired by operating the acquiring module. Operating the collision avoidance module may further comprise implementing at least one collision avoidance algorithm based on at least one acoustic data.

[0097] Furthermore, operating the collision avoidance module may comprise operating an object detection module, wherein operating the one object detection module may comprise detecting at least one obstacle. Operating the object detection module may also comprise detecting at least one obstacle according to at least one data, wherein the at least one data may comprise at least one data related to at least one data acquired by operating the acquiring module. Operating the at least one object detection module may further comprise outputting at least one operation command to the at least one actuator, wherein the at least one operational command may comprise at least one command to output at least one acoustic signal. The at least one data may further comprise at least one of, but not limited to at least one map data, at least one GPS data, at least one IMU data, at least one triangulation result according to at least one weather data and GPS data. The at least one map data may comprise but is not limited to at least one landmark, and / or at least one 3D point / scan. The at least one IMU / GPS data may be considered as a point of origin based on a map data for the object detection result outputted by the object detection module. The point of origin may be updated with respect to the output of the operation of the object detection module. This constitutes a preferred advantage of the present invention as the present invention may increase the reliability of an IMU data point without necessarily relying on GPS data.

[0098] Moreover, operating the at least one object detection module may comprise detecting at least one obstacle according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0099] Operating the object detection module may comprise improving the accuracy of the location of the obstacle by making use of a change-point detection algorithm, a breakpoint detection algorithm, and / or an outlier detection algorithm.

[0100] These algorithms may comprise checking for implausible jumps or unphysical behavior in order to improve an estimation of the location and also improve the differentiation between primary signals and reflected signals. These algorithms may also comprise invalidating and / or validating at least one object detection data generated by the object detection module.

[0101] Operating the object detection module may comprise generating at least one object detection data, wherein operating the object detection module may comprise implementing at least one of, but not limited to, TDOA (time difference of arrival), beamforming, MUSIC (Multiple Signal Classification) or other approaches. Operating the object detection module may also comprise augmenting the accuracy of the object detection data by making use of a change-point or break-point or outlier detection algorithm.

[0102] Operating the object detection module may further comprise extracting obstacle-direct noise data from the at least one secondary acoustic data, wherein obstacle-direct noise data may comprise at least one non-reflected acoustic noise generated by at least one obstacle / object. Operating the object detection module may additionally comprise extracting obstacle-reflected noise data from the at least one secondary acoustic data, wherein obstacle-reflected noise data may comprise at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by at least one obstacle / object.

[0103] Operating the object detection module may comprise extracting the obstacle-direct noise data and / or the obstacle-reflected noise data according to metadata related to at least one data, wherein the at least one data may result in at least part of the at least one secondary acoustic data, wherein the at least one data may have been detected with respect to the position of the at least one sensor and / or the position of the obstacle / object. Operating the object detection module may also comprise extracting the obstacle-direct noise data and / or the obstacle -reflected noise data according to, but not limited to, the angle the at least one data resulting in in at least part of the at least one secondary acoustic data, the magnitude of the at least one data resulting in at least part of the at least one secondary acoustic data, the time of arrival the at least one data resulting in at least part of the at least one secondary acoustic data. Operating the object detection module may further comprise extracting the obstacle-direct noise data and / or the obstacle-reflected noise data by making use of at least one Al algorithm, such as but not limited to deep learning methods. Non-AI algorithm may comprise but not be limited to correlation algorithms, matching pursuit, orthogonal matching pursuit, basis-pursuit denoising. Operating the object detection module may additionally comprise identifying the location of the at least one obstacle according to at least one map / GPS / IMU data and at least one self-reflected noise data. Operating the object detection module may also comprise identifying the location of the at least one obstacle according to at least one map / GPS / IMU data and at least one self-reflected noise data by making use of at least one data-driven Al algorithm.

[0104] Additionally and alternatively, the object detection module may comprise an object movement prediction module, wherein operating the object movement prediction module may comprise predicting at least one movement of at least one object. Operating the object movement prediction may comprise utilizing at least one of, but not limited to Kalman filters and polynomial fitting, and / or Optical flow. Operating the object movement prediction module may also comprise predicting at least one movement of at least one object according to at least one Al algorithm, such as but not limited to particle filter, RNN (recurrent neural network), GPR (gaussian process regression), MDP (Markov decision process), GAN, LSTM or more particularly a variant of social-LSTM for a lot of objects, transformer models, multimodal transformer models for sensor fusion.

[0105] In another embodiment, the collision avoidance module may comprise an area monitoring module, wherein operating the area monitoring module may comprise mapping at least one area according to at least one data. Operating the area monitoring module may also comprise mapping at least one area according to at least one data, wherein the at least one data may comprise at least one data related to at least one data acquired by operating the acquiring module. The at least one data may comprise at least one image data. Operating the area monitoring module may comprise operating at least one Al module, wherein the at least one Al module may comprise a neuromorphic neural network. Operating the at least one Al module may comprise mapping at least one area according to the at least one image data. Operating the at least one Al-module may also comprise implementing at least one Tiny Object Detection algorithm. Operating the area monitoring module may further comprise mapping at least one area according to at least one acoustic data. Operating the area monitoring module may additionally comprise mapping at least one area according to at least one SLAM (Simultaneous Localization and Mapping) algorithm.

[0106] In a further embodiment, the collision avoidance module may comprise a navigation module, wherein operating the navigation module may comprise outputting at least one navigation command to the external and / or outer system.

[0107] Additionally and alternatively, operating the modules operated by the method, according to any of the preceding embodiments, separately. The method may also comprise implementing at least one vibration suppression technique to suppress the vibrations caused by the at least one motor. The method may further comprise operating the modules operated by the method according to any of the preceding embodiments, independently of their location with respect to the external and / or outer system. For examples, some modules may be located on a server. Operating the modules of the method may additionally comprise transmitting and / or receiving at least one data.

[0108] Furthermore, the method may comprise operating a noise reflection detection module, wherein operating the noise reflection detection module may comprise performing the at least one function related to at least one reflected data described in the description of the present invention. The method may comprise matching at least one direct / original signal to at least one reflected signal according to at least one cross-correlation algorithm, such that the cross-correlation algorithm comprises measuring similarity between data. The method may also comprise matching at least one direct / original signal to at least one reflected signal according to at least one matched filter, such that the at least one matched filter comprises detecting known signals in noisy environments. The method may further comprise detecting at least one direct / original signal and / or at least one reflected signal according to at least one blind source separation algorithm, such as but not limited ICA (Independent Component Analysis), and / or PCA (Principal Component Analysis).

[0109] The method may comprise differentiating between at least one direct / original signal and / or at least one reflected signal according to at least one time frequency analysis algorithm, such as but not limited to STFT (Short-Time Fourier Transform). The method may comprise differentiating between at least one direct / original signal and / or at least one reflected signal according to at least one, but not limited to, matching pursuit algorithm and / or Orthogonal matching pursuit and / or Basis-pursuit.

[0110] Moreover, operating the method may comprise differentiating between at least one direct / original signal and / or at least one refracted signal. The method may comprise differentiating between at least one direct / original signal and / or at least one refracted signal by making use of the method as described in the description of the present invention replacing the reflected signal with a refracted signal. The method may also comprise improving the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm. These algorithms may comprise checking for implausible jumps or unphysical behavior in order to improve an estimation of the data and also improve the differentiation between direct signals and refracted signals. These algorithms may also comprise invalidating and / or validating at least one object detection data generated by the apparatus.

[0111] The method may also comprise identifying the medium in which the external and / or outer system is present an / or the medium in which the external and / or outer system is predicted to enter. The method may further comprise identifying the medium in which the external and / or outer system is present according to at least one data, wherein the at least one data may comprise at least one reflected and / or refracted data. Examples of the medium may comprise but is not limited to the seabed under water, streets or soil or water under the external and / or outer system, the medium's composition, texture, consistency, properties and conditions.

[0112] Additionally, operating the discriminator module may comprise storing acoustic noise generated by at least one type of system and / or at least one obstacle. Operating the at least one actuator may also comprise outputting at least one acoustic signal, wherein the at least one acoustic signal corresponds to at least one acoustic noise generated by at least one type of system and / or at least one obstacle, wherein the at least one type of system may correspond to at least one of any type of system mentioned previously herein. The method may thus serve the double purpose of noise cancellation and camouflaging the generated noise of the operated modules and / or components as another type of system independently of parameters that may affect the acoustic noise generated such as but not limited to the size of the system and / or components. The method may further comprise modifying the at least one acoustic signal.

[0113] The present invention is also described by the following numbered embodiments.

[0114] Below, apparatus embodiments will be discussed. These embodiments are abbreviated by the letter "A" followed by a number. When reference is herein made to an apparatus embodiment, those embodiments are meant.

[0115] Al. An apparatus for an external system and / or an outer system, wherein the apparatus comprises, a discriminator module, and a localization module.

[0116] A2. The apparatus according to the preceding apparatus embodiment wherein the apparatus comprises a noise cancellation module.

[0117] A3. The apparatus according to any of the preceding apparatus embodiments wherein the apparatus comprises an acquiring module.

[0118] A4. The apparatus according to any of the preceding apparatus embodiments wherein the apparatus comprises a processing module.

[0119] A5. The apparatus according to any of the preceding apparatus embodiments wherein the apparatus comprises a collision avoidance module.

[0120] A6. The apparatus according to any of the preceding apparatus embodiment, wherein the external system is a system manufactured without the apparatus and the outer system is a system manufactured with the apparatus.

[0121] A7. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or the outer system comprise at least one motor.

[0122] A8. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or the outer system comprise at least one vehicle.

[0123] A9. The apparatus according to any of the preceding apparatus embodiments wherein the external system comprises an aircraft and / or an aircraft system, and / or the outer system comprises an aircraft and / or an aircraft system.

[0124] A10. The apparatus according to any of the preceding apparatus embodiments, wherein the external system comprises a watercraft and / or a watercraft system, and / or the outer system comprises a watercraft and / or a watercraft system.

[0125] All. The apparatus according to any of the preceding apparatus embodiments wherein the external system comprises at least one propeller and / or a propeller system, and / or the outer system comprises at least one propeller and / or a propeller system.

[0126] A12. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or outer system comprise an airship.

[0127] A13. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or outer system comprise a ship. A14. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or outer system comprise a drone.

[0128] A15. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or outer system comprise a vessel.

[0129] A16. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or outer system comprise a multicopter drone.

[0130] A17. The apparatus according to any of the preceding apparatus embodiments, wherein the external system and / or outer system comprise a submarine.

[0131] A18. The apparatus according to any of the preceding apparatus embodiments, wherein the apparatus is a retrofit for the external system.

[0132] A19. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for an aircraft and / or an aircraft system.

[0133] A20. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for a watercraft and / or watercraft system.

[0134] A21. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for an airship.

[0135] A22. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for a ship.

[0136] A23. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for a drone.

[0137] A24. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for a vessel. A25. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for a multicopter drone.

[0138] A26. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A18, wherein the apparatus is a retrofit for a submarine.

[0139] A27. The apparatus according to any of the preceding apparatus embodiments, wherein the apparatus is a noise cancellation apparatus.

[0140] A28. The apparatus according to any of the preceding apparatus embodiments, wherein the apparatus is a vehicle localization apparatus.

[0141] A29. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A8 and / or A28, wherein a vehicle comprises a machine capable of movement.

[0142] A30. The apparatus according to any of the preceding apparatus embodiments, wherein the apparatus is an obstacle localization apparatus.

[0143] A31. The apparatus according to any of the preceding apparatus embodiments wherein the apparatus's modules are configured for analog computing.

[0144] A32. The apparatus according to any of the preceding apparatus embodiments wherein at least one of the apparatus's modules is configured for analog computing.

[0145] A33. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A31, wherein the apparatus's modules are configured for neuromorphic computing.

[0146] A34. The apparatus according to any of the preceding apparatus embodiments with the features of A32, wherein at least one of the apparatus's modules is configured for neuromorphic computing.

[0147] A35. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A3, wherein the acquiring module is configured to acquire at least one data. A36. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one data in real-time.

[0148] A37. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one image data.

[0149] A38. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one acoustic data.

[0150] A39. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one wind data.

[0151] A40. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one meteorological data.

[0152] A41. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one atmospheric pressure data.

[0153] A42. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one rotation rate data wherein the rotation rate data comprises at least one data related to the rotation rate of the external and / or outer system.

[0154] A43. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one acceleration data wherein the acceleration data comprises at least one data related to the kinematic acceleration of the external and / or outer system.

[0155] A44. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one voltage data wherein the voltage data relates to the voltage of the external system and / or outer system.

[0156] A45. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A7 and A35, wherein the acquiring module is configured to acquire at least one rotational speed data wherein the at least one rotational speed data relates to the at least one motor.

[0157] A46. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35, wherein the acquiring module is configured to acquire at least one humidity data.

[0158] A47. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A3, wherein the acquiring module comprises at least one analog sensor.

[0159] A48. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A3, wherein the acquiring module comprises at least one digital sensor.

[0160] A49. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A3 and, A33 and / or A34, wherein the acquiring module comprises at least one neuromorphic sensor.

[0161] A50. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A35 and any combination of apparatus embodiments A47-A49 wherein the at least one sensor is configured to acquire the at least one data.

[0162] A51. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment(s) A35 and / or A50, wherein the acquiring module is configured to output the at least one data to the processing module.

[0163] A52. The apparatus according to any of the preceding apparatus embodiments, with the features of apparatus embodiment A4, wherein the processing module is configured to process at least one data into an at least one processed data.

[0164] A53. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A52, wherein the processing module is configured to process the at least one data into at the at least one processed data wherein the processed data comprises at least one response data.

[0165] A54. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A34, A50, and A52, in combination with A47 and / or A48 wherein the processing module is configured to convert the at least one data into at least one neuromorphic data wherein the at least one neuromorphic data comprises the at least one data converted to be correctly read by a neuromorphic chip.

[0166] A55. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A52, wherein the processing module is configured to output the at least one processed data to the noise cancellation module.

[0167] A56. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A52, wherein the processing module is configured to output the at least one processed data to the discriminator module.

[0168] A57. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A52, wherein the processing module is configured to output the at least one processed data to the collision avoidance module.

[0169] A58. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancellation module is configured to compensate for acoustic noise generated by the external and / or outer system.

[0170] A59. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancellation module is configured to compensate for acoustic noise generated by the external and / or outer system according to at least one data.

[0171] A60. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A52 and A59, wherein the noise cancellation module is configured to compensate for acoustic noise generated by the external and / or outer system according to at least one processed data.

[0172] A61. The apparatus according to any of the preceding apparatus embodiment with the features of apparatus embodiment A59, wherein the at least one data comprises at least one acoustic data.

[0173] A62. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancellation module is configured to perform acoustic noise cancellation.

[0174] A63. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A59 and A62, wherein active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective is comprised outside of either the apparatus or the external and / or outer system.

[0175] A64. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancellation module is configured to perform sectorial noise cancellation.

[0176] A65. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancellation module is configured to perform sectorial active noise cancellation.

[0177] A66. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A59 and A65, wherein sectorial active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective in comprised outside of either the apparatus or the external and / or outer system wherein the outside perspective is comprised in a predetermined positional sector.

[0178] A67. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A59 and, A62 and / or A65 wherein the noise cancellation module is configured to reduce acoustic noise depicted in the at least one data by at least lOdB by making use of active noise cancellation and / or sectorial active noise cancellation.

[0179] A68. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A59 and, A63 and / or A66, wherein the noise cancellation module is configured to reduce the acoustic noise depicted in the at least one primary acoustic data by at least lOdB according to the outside perspective.

[0180] A69. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancellation module is configured to output at least one noise cancellation command, wherein the at least one noise cancellation command is configured to compensate for acoustic noise generated by the external and / or outer system.

[0181] A70. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A69, wherein the at least one noise cancellation command is configured to be adjustable.

[0182] A71. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A70, wherein the at least one noise cancellation command is configured to be adaptively adjustable according to the external and / or outer system.

[0183] A72. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A61, wherein the at least one noise cancellation command is configured according to the content, the magnitude frequency response and / or phase response of the at least one acoustic data.

[0184] A73. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A61, wherein the noise cancellation module is configured to filter out wind noise and / or aeroacoustic effects from the at least one acoustic data.

[0185] A74. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A73, wherein the noise cancellation module is configured to compensate for acoustic noise generated by the external and / or outer system according to at least one acoustic data, wherein the at least one acoustic data is configured to have been filtered to remove wind noise and / or aeroacoustic effects.

[0186] A75. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A62, wherein the noise cancellation module is configured to generate at least one counter-noise signal. A76. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A59 and A75, wherein the noise cancellation module is configured to generate at least one counter-noise signal according to the at least one data.

[0187] A77. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A75, wherein the noise cancellation module is configured to generate the at least one counter-noise signal according to at least one feedforward counter-noise generating algorithm.

[0188] A78. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A75, wherein the noise cancellation module is configured to generate the at least one counter-noise signal according to at least one feedback counter-noise generating algorithm.

[0189] A79. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A75, wherein the noise cancellation module is configured to generate the at least one counter-noise signal according to at least one hybrid counter-noise generating algorithm.

[0190] A80. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A62, wherein the noise cancellation module is configured to adaptively generate at least one counter-noise signal.

[0191] A81. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A62, wherein the noise cancellation module is configured to generate at least one counter-noise signal according to at least one Al algorithm.

[0192] A82. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A2, wherein the noise cancelling module is configured to predict acoustic noise to be generated by the external and / or outer system.

[0193] A83. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A62 and A82 wherein the noise cancellation module is configured to perform acoustic noise cancellation according to the prediction of the acoustic noise generated by the external and / or outer system.

[0194] A84. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A82, wherein the prediction is generated by at least one Al algorithm.

[0195] A85. The apparatus according to any of the preceding apparatus embodiments, with the features of embodiment A2, wherein the noise cancellation module is configured to set up its parameters according to at least one Al algorithm.

[0196] A86. The apparatus according to any of the preceding apparatus embodiments, with the features of apparatus embodiments A58 or A59, and A85, wherein the noise cancellation module is configured to set up its parameters for the compensation of acoustic noise according to at least one Al algorithm.

[0197] A87. The apparatus according to any of the preceding apparatus embodiments wherein the apparatus comprises at least one actuator.

[0198] A88. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A87, wherein the at least one actuator comprises at least one loudspeaker.

[0199] A89. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A87, wherein the at least one actuator comprises at least one motor.

[0200] A90. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A89, wherein the at least one external and / or outer system comprises the at least one motor.

[0201] A91. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A2 and A87, wherein the noise cancelling module comprises at least one actuator.

[0202] A92. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A91 wherein the at least one actuator is configured to perform active noise cancellation and / or sectorial active noise cancellation.

[0203] A93. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A91 wherein the at least one actuator is configured to perform active noise cancellation and / or sectorial active noise cancellation according to at least one noise cancellation command.

[0204] A94. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A69 and A91 wherein the at least one actuator is configured to perform active noise cancellation and / or sectorial active noise cancellation according to the at least one noise cancellation command.

[0205] A95. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A63 and / or A66, and A91 wherein the at least one actuator is configured to perform active noise cancellation and / or sectorial active noise cancellation according to the outside perspective.

[0206] A96. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A87, wherein the at least one actuator is configured to output at least one acoustic signal.

[0207] A97. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A3 and A87, wherein the acquiring module is configured to detect at least one acoustic signal and / or at least one reflected acoustic signal outputted by the at least one actuator.

[0208] A98. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A2 and A87, wherein the at least one actuator is configured to be calibrated according to the noise cancellation module.

[0209] A99. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A87, wherein the at least one actuator is configured to be calibrated according to the localization module.

[0210] A100. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A5 and A87, wherein the at least one actuator is configured to be calibrated according to the collision avoidance module.

[0211] A101. The apparatus according to any of the preceding apparatus embodiments wherein the discriminator module is configured to differentiate acoustic noise generated by at least one type of system from other acoustic noises.

[0212] A102. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the discriminator module is configured to differentiate acoustic noise generated by at least one type of system from other acoustic noises according to at least one acoustic data.

[0213] A103. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A101, wherein the at least one type of system comprises at least one motor.

[0214] A104. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the at least one type of system comprises at least one vehicle.

[0215] A105. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the at least one type of system comprises an aircraft and / or an aircraft system.

[0216] A106. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the at least one type of system comprises a watercraft and / or a watercraft system.

[0217] A107. The apparatus according to any of the preceding apparatus embodimentss with the features of apparatus embodiment A101, wherein the at least one type of system comprises at least one propeller and / or a propeller system.

[0218] A108. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the at least one type of system comprises an airship.

[0219] A109. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the at least one type of system comprises a ship.

[0220] A110. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the at least one type of system comprises a drone.

[0221] Alli. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A101, wherein the at least one type of system comprises a vessel. Al 12. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A101, wherein the at least one type of system comprises a multicopter drone.

[0222] Al 13. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A101, wherein the at least one type of system comprises a submarine.

[0223] Al 14. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the discriminator module is configured to output at least one primary acoustic data wherein the at least one primary acoustic data relates to at least one acoustic noise generated by the at least one type of system.

[0224] Al 15. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A101, wherein the discriminator module is configured to output at least one secondary acoustic data, wherein the at least one secondary acoustic data relates to other acoustic noises, wherein other acoustic noises relate to at least one acoustic noise not generated by the at least one type of system.

[0225] Al 16. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 14, wherein the discriminator module is configured to output the at least one primary acoustic data to the localization module.

[0226] Al 17. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 16, wherein the discriminator module is configured to output the at least one secondary acoustic data to the collision avoidance module.

[0227] Al 18. The apparatus according to any of the preceding apparatus embodiments wherein the localization module is configured to differentiate acoustic noise generated by the external and / or outer system from at least one 3rdparty system.

[0228] Al 19. The apparatus according to any of the preceding apparatus embodiments wherein the localization module is configured to locate the external and / or outer system and / or at least one 3rdparty system. A120. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 19, wherein the localization module is configured to locate the external and / or outer system and / or at least one 3rdparty system with respect to each other.

[0229] A121. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 19, wherein the localization module is configured to locate the external and / or outer system and / or at least one 3rdparty system with respect to at least one data.

[0230] A122. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 19, wherein the localization module is configured to locate the external and / or outer system and / or at least one 3rdparty system according to at least one data.

[0231] A123. The apparatus according to any of the preceding apparatus embodiments with the features of embodiments A121 and / or A122, wherein the at least one data comprises at least one data related to at least one data according to any of apparatus embodiments A37-A46.

[0232] A124. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 18, wherein the localization module is configured to differentiate acoustic noise generated by the external and / or outer system from at least one 3rdparty system according to at least one data.

[0233] A125. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A118-A124, wherein the at least one 3rdparty system is configured as at least one type of system according to at least one of any of the preceding apparatus embodiments A103-A113.

[0234] A126. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A122 and / or A124 wherein the at least one data comprises at least one acoustic data.

[0235] A127. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A126, wherein the at least one acoustic data comprises at least one acoustic noise data.

[0236] A128. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments Al 16 and, A122 and / or A124 wherein the at least one data comprises the at least one primary acoustic data.

[0237] A129. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments Al 18, wherein the localization module is configured to output at least one 3rdparty system acoustic data wherein the at least one 3rdparty system acoustic data relates to acoustic noise generated by the at least one 3rdparty system.

[0238] A130. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments Al 18, wherein the localization module is configured to output at least one self-system acoustic data wherein the at least one self-system acoustic data relates to acoustic noise generated by the external system and / or the outer system.

[0239] A131. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the localization module is configured to transmit and / or receive at least one data to and / or from the collision avoidance module.

[0240] A132. The apparatus according to any of the preceding apparatus embodiments, wherein the localization module is configured to output at least one data to the external and / or outer system.

[0241] A133. The apparatus according to any of the preceding apparatus embodiments, wherein the localization module is configured to output at least one data to an external device / server / service independent of the apparatus according to any of the preceding apparatus embodiments.

[0242] A134. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A63 and / or A66, wherein the localization module is configured to output at least one location data to the noise cancellation module, wherein the at last one location data relates to the outside perspective.

[0243] A135. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, A121, A122, wherein the localization module is configured to receive the at least one data from the collision avoidance module. A136. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 19, wherein the localization module is configured to improve the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0244] A137. The apparatus according to any of the preceding apparatus embodiments wherein the localization module comprises a 3rdparty localization module.

[0245] A138. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A137, wherein the 3rdparty localization module is configured to locate at least one 3rdparty system based on apparatus embodiment A125 wherein the at least one 3rdparty system does not comprise the external system and / or outer system.

[0246] A139. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A129 and A138, wherein the 3rdparty localization module is configured to locate at least one 3rdparty system according to the at least one 3rdparty system acoustic data.

[0247] A140. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A87 and A137, wherein the 3rdparty localization module is configured to output at least one operation command to the at least one actuator.

[0248] A141. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A140, wherein the at least one operational command comprises at least one command configured to output at least one acoustic signal.

[0249] A142. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A51, A57, A97 and A141, wherein the 3rdparty localization module is configured to detect at least one 3rdparty system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0250] A143. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A51, A57, A97 and A141wherein the 3rdparty localization module is configured to detect at least one 3rdparty system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0251] A144. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A139, wherein the 3rdparty localization module is configured to differentiate 3rdparty direct noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty direct noise data comprises at least one non-reflected acoustic noise generated by at least one 3rdparty system.

[0252] A145. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A139, wherein the 3rdparty localization module is configured to differentiate 3rdparty reflected noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty reflected noise data comprises at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the at least one 3rdparty system.

[0253] A146. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment(s) A144 and / or A145, wherein the 3rdparty localization module is configured to differentiate the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to metadata related to at least one data, wherein the at least one data results in the at least one 3rdparty system acoustic data.

[0254] A147. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A50 and A146, wherein the at least one data was detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system.

[0255] A148. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A146, wherein the 3rdparty localization module is configured to extract the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the angle the at least one data resulting in the at least one 3rdparty system acoustic data was detected at.

[0256] A149. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A146, wherein the 3rdparty localization module is configured to extract the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the magnitude of the at least one data resulting in the at least one 3rdparty system acoustic data. A150. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A146, wherein the 3rdparty localization module is configured to extract the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the time of arrival the at least one data resulting in the at least one 3rdparty system acoustic data was detected at.

[0257] A151. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A146 wherein the 3rdparty localization module is configured to extract the 3rdparty direct noise data and / or the 3rdparty reflected noise data by making use of at least one Al algorithm.

[0258] A152. The apparatus according to any of the preceding apparatus embodiments wherein the localization module comprises a self-localization module.

[0259] A153. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A152, wherein the self-localization module is configured to locate the external system and / or the outer system.

[0260] A154. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A130 and A153, wherein the self-localization module is configured to locate the external system and / or the outer system according to the at least one self-system acoustic data.

[0261] A155. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A87 and A153, wherein the self-localization module is configured to output at least one operation command to the at least one actuator.

[0262] A156. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A155, wherein the at least one operational command comprises at least one command configured to output at least one acoustic signal.

[0263] A157. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A97 and A156, wherein the self-localization module is configured to detect the external and / or system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0264] A158. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A51, A57, A97 and A156, wherein the selflocalization module is configured to detect the external and / or outer system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0265] A159. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A154, wherein the self-localization module is configured to extract self-direct noise data from the at least one self-system acoustic data, wherein self-direct noise data comprises at least one non-reflected acoustic noise generated by the external and / or outer system.

[0266] A160. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A154, wherein the self-localization module is configured to extract self-reflected noise data from the at least one self-system acoustic data, wherein self-reflected noise data comprises at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the external and / or outer system.

[0267] A161. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment(s) A159 and / or A160, wherein the selflocalization module is configured to extract the self-direct noise data and / or the self-reflected noise data according to metadata related to at least one data, wherein the at least one data results in the at least one self-system acoustic data.

[0268] A162. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A50 and A161, wherein the at least one data was detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system.

[0269] A163. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161, wherein the self-localization module is configured to extract the self-direct noise data and / or the self-reflected noise data according to the angle the at least one data resulting in the at least one selfsystem acoustic data was detected at.

[0270] A164. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161, wherein the self-localization module is configured to extract the self-direct noise data and / or the self-reflected noise data according to the magnitude of the at least one data resulting in the at least one self-system acoustic data.

[0271] A165. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161, wherein the self-localization module is configured to extract the self-direct noise data and / or the self-reflected noise data according to the time of arrival the at least one data resulting in the at least one self-system acoustic data was detected at.

[0272] A166. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161 wherein the self-localization module is configured to extract the self-direct noise data and / or the self-reflected noise data by making use of at least one Al algorithm.

[0273] A167. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the collision avoidance module is configured to output at least one navigation command to the external system and / or the outer system.

[0274] A168. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the collision avoidance module is configured to implement at least one collision avoidance algorithm based on at least one data.

[0275] A169. The apparatus according to any of the preceding apparatus embodiments with the features of embodiments A168, wherein the at least one data comprises at least one data related to at least one data according to any of apparatus embodiments A37-A46.

[0276] A170. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the collision avoidance module is configured to implement at least one collision avoidance algorithm based on at least one acoustic data.

[0277] A171. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the collision avoidance module comprises an object detection module.

[0278] A172. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A171, wherein the at least one object detection module is configured to detect at least one obstacle.

[0279] A173. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A172, wherein the at least one object detection module is configured to detect at least one obstacle according to at least one data.

[0280] A174. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A173, wherein the at least one data comprises at least one data related to at least one data according to any of apparatus embodiments A37-A46.

[0281] A175. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A87 and A172, wherein the at least one object detection module is configured to output at least one operation command to the at least one actuator.

[0282] A176. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A175, wherein the at least one operational command comprises at least one command to output at least one acoustic signal.

[0283] A177. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A97 and A176, wherein the at least one object detection module is configured to detect at least one obstacle according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0284] A178. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A51, A57, A97 and A176, wherein the at least one object detection module is configured to detect at least one obstacle according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0285] A179. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A172, wherein the object detection module is configured to improve the accuracy of the location of the obstacle by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0286] A180. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 17, wherein the object detection module is configured to extract obstacle-direct noise data from the at least one secondary acoustic data, wherein obstacle-direct noise data comprises at least one nonreflected acoustic noise generated by at least one obstacle / object.

[0287] A181. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment Al 17, wherein the object detection module is configured to extract obstacle-reflected noise data from the at least one secondary acoustic data, wherein obstacle-reflected noise data comprises at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by at least one obstacle / object.

[0288] A182. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment(s) A180 and / or A181, wherein the object detection module is configured to extract the obstacle-direct noise data and / or the obstacle-reflected noise data according to metadata related to at least one data, wherein the at least one data results in at least part of the at least one secondary acoustic data.

[0289] A183. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A50 and A181, wherein the at least one data was detected with respect to the position of the at least one sensor and / or the position of the obstacle / object.

[0290] A184. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A182, wherein the object detection module is configured to extract the obstacle-direct noise data and / or the obstacle -reflected noise data according to the angle the at least one data resulting in in at least part of the at least one secondary acoustic data.

[0291] A185. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161, wherein the object detection module is configured to extract the obstacle -direct noise data and / or the obstacle -reflected noise data according to the magnitude of the at least one data resulting in at least part of the at least one secondary acoustic data

[0292] A186. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161, wherein the object detection module is configured to extract the obstacle -direct noise data and / or the obstacle-reflected noise data according to the time of arrival the at least one data resulting in at least part of the at least one secondary acoustic data.

[0293] A187. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A161 wherein the object detection module is configured to extract the obstacle-direct noise data and / or the obstacle-reflected noise data by making use of at least one Al algorithm.

[0294] A188. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A171, wherein the object detection module comprises an object movement prediction module.

[0295] A189. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A188, wherein the object movement prediction module is configured to predict at least one movement of at least one object.

[0296] A190. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A189, wherein the object movement prediction module is configured to predict at least one movement of at least one object according to at least one Al algorithm.

[0297] A191. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the collision avoidance module comprises an area monitoring module.

[0298] A192. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A191, wherein the area monitoring module is configured to map at least one area.

[0299] A193. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A192, wherein the area monitoring module is configured to map at least one area according to at least one data.

[0300] A194. The apparatus according to any of the preceding apparatus embodiments with the features of embodiment A193, wherein the at least one data comprises at least one data related to at least one data according to any of apparatus embodiments A37-A46.

[0301] A195. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A193, wherein the at least one data comprises at least one image data.

[0302] A196. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A191, wherein the area monitoring module comprises at least one Al module.

[0303] A197. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A196, wherein the at least one Al module is configured as a neuromorphic neural network.

[0304] A198. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiments A195 and A196 wherein the at least one Al module is configured to map at least one area according to the at least one image data.

[0305] A199. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A196, wherein the at least one Al-module is configured to implement at least one Tiny Object Detection algorithm.

[0306] A200. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A191, wherein the area monitoring module is configured to map at least one area according to at least one acoustic data.

[0307] A201. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A191, wherein the area monitoring module is configured to map at least one area according to at least one SLAM algorithm.

[0308] A202. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A192, wherein the area monitoring module is configured to map at least one area according to at least one data.

[0309] A203. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A5, wherein the collision avoidance module comprises a navigation module.

[0310] A204. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A203, wherein the navigation module is configured to output at least one navigation command to the external and / or outer system. A205. The apparatus according to any of the preceding apparatus embodiments, wherein the modules of the apparatus are configured to operate separately.

[0311] A206. The apparatus according to any of the preceding apparatus embodiments, with the features of apparatus embodiments A7, A89, or A103, wherein the apparatus is configured to implement at least one vibration suppression technique to suppress the vibrations caused by the at least one motor.

[0312] A207. The apparatus according to any of the preceding apparatus embodiments, wherein the modules of the apparatus are configured to operate independently of their location with respect to the external and / or outer system.

[0313] A208. The apparatus according to any of the preceding apparatus embodiments, wherein the modules of the apparatus are configured to transmit and / or receive at least one data.

[0314] A209. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A143-A151, A158-A166, A178-A187, wherein the apparatus comprises a noise reflection detection module, wherein the noise reflection detection module is configured to perform the at least one function described in any of the cited apparatus embodiments.

[0315] A210. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A143-A151, A158-A166, A178-A187, or A209, wherein the apparatus is configured to match at least one direct / original signal to at least one reflected signal according to at least one cross-correlation algorithm.

[0316] A211. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A143-A151, A158-A166, A178-A187, or A209, wherein the apparatus is configured to match at least one direct / original signal to at least one reflected signal according to at least one matched filter.

[0317] A212. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A143-A151, A158-A166, A178-A187, or A209, wherein the apparatus is configured to detect at least one direct / original signal and / or at least one reflected signal according to at least one blind source separation algorithm. A213. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A143-A151, A158-A166, A178-A187, or A209, wherein the apparatus is configured to differentiate between at least one direct / original signal and / or at least one reflected signal according to at least one time frequency analysis algorithm.

[0318] A214. The apparatus according to any of the preceding apparatus embodiments with the features of any of apparatus embodiments A143-A151, A158-A166, A178-A187, or A209, wherein the apparatus is configured to differentiate between at least one direct / original signal and / or at least one reflected signal according to at least one matching pursuit algorithm.

[0319] A215. The apparatus according to any of the preceding apparatus embodiments, wherein the apparatus is configured to differentiate between at least one direct / original signal and / or at least one refracted signal.

[0320] A216. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment A215, wherein the apparatus is configured to differentiate between at least one direct / original signal and / or at least one refracted signal by making use of the apparatus as described in any of apparatus embodiments A143-A151, A158-A166, A178-A187, replacing the reflected signal with a refracted signal.

[0321] A217. The apparatus according to any of the preceding apparatus embodiments with the features of apparatus embodiment(s) A138 and / or A153, wherein the apparatus is configured to improve the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0322] A218. The apparatus according to any of the preceding embodiments with the features of embodiment A101, wherein the discriminator module is configured to store acoustic noise generated by at least one type of system and / or at least one obstacle.

[0323] A219. The apparatus according to any of the preceding embodiments with the features of embodiment A96, wherein the at least one actuator is configured to output at least one acoustic signal, wherein the at least one acoustic signal corresponds to at least one acoustic noise generated by at least one type of system and / or at least one obstacle.

[0324] A220. The apparatus according to any of the preceding embodiments, with the features of any of embodiments A218-A219, wherein the at least one type of system correspond to at least one of any type of system mentioned in preceding apparatus embodiments A103-A113.

[0325] A221. The apparatus according to any of the preceding embodiments with the features of embodiment A96, wherein the system is configured to modify the at least one acoustic signal.

[0326] Below, method embodiments will be discussed. These embodiments are abbreviated by the letter "M" followed by a number. When reference is herein made to a method embodiment, those embodiments are meant.

[0327] Ml. A method for an external system and / or an outer system, wherein the method comprises, operating a discriminator module, and operating a localization module.

[0328] M2. The method according to the preceding method embodiment wherein the method comprises operating a noise cancellation module.

[0329] M3. The method according to the preceding method embodiment wherein the method comprises operating an acquiring module.

[0330] M4. The method according to any of the preceding method embodiments wherein the method comprises operating a processing module.

[0331] M5. The method according to any of the preceding method embodiments wherein the method comprises operating a collision avoidance module.

[0332] M6. The method according to any of the preceding method embodiment, wherein the external system is a system manufactured without the modules operated by the method and the outer system is a system manufactured with the modules operated by the method.

[0333] M7. The method according to any of the preceding method embodiments, wherein the external system and / or the outer system comprise at least one motor.

[0334] M8. The method according to any of the preceding method embodiments, wherein the external system and / or the outer system comprise at least one vehicle.

[0335] M9. The method according to any of the preceding method embodiments wherein the external system comprises an aircraft and / or an aircraft system, and / or the outer system comprises an aircraft and / or an aircraft system.

[0336] MIO. The method according to any of the preceding method embodiments, wherein the external system comprises a watercraft and / or a watercraft system, and / or the outer system comprises a watercraft and / or a watercraft system.

[0337] Mil. The method according to any of the preceding method embodiments wherein the external system comprises at least one propeller and / or a propeller system, and / or the outer system comprises at least one propeller and / or a propeller system.

[0338] M12. The method according to any of the preceding method embodiments, wherein the external system and / or outer system comprise an airship.

[0339] M13. The method according to any of the preceding method embodiments, wherein the external system and / or outer system comprise a ship.

[0340] M14. The method according to any of the preceding method embodiments, wherein the external system and / or outer system comprise a drone.

[0341] M15. The method according to any of the preceding method embodiments, wherein the external system and / or outer system comprise a vessel.

[0342] M16. The method according to any of the preceding method embodiments, wherein the external system and / or outer system comprise a multicopter drone.

[0343] M17. The method according to any of the preceding method embodiments, wherein the external system and / or outer system comprise a submarine.

[0344] M18. The method according to any of the preceding method embodiments, wherein the method comprises operating a retrofit for the external system.

[0345] M19. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for an aircraft and / or an aircraft system.

[0346] M20. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for a watercraft and / or watercraft system.

[0347] M21. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for an airship.

[0348] M22. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for a ship.

[0349] M23. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for a drone.

[0350] M24. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for a vessel.

[0351] M25. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for a multicopter drone.

[0352] M26. The method according to any of the preceding method embodiments with the features of method embodiment M18, wherein the method comprises operating a retrofit for a submarine.

[0353] M27. The method according to any of the preceding method embodiments, wherein the method is a noise cancellation method.

[0354] M28. The method according to any of the preceding method embodiments, wherein the method is a vehicle localization method.

[0355] M29. The method according to any of the preceding method embodiments with the features of method embodiments M8 and / or M28 , wherein a vehicle comprises a machine capable of movement.

[0356] M30. The method according to any of the preceding method embodiments, wherein the method is an obstacle localization method.

[0357] M31. The method according to any of the preceding method embodiments wherein the method's modules are configured for analog computing.

[0358] M32. The method according to any of the preceding method embodiments wherein at least one of the method's modules is configured for analog computing.

[0359] M33. The method according to any of the preceding method embodiments with the features of method embodiment M31, wherein the method's modules are configured for neuromorphic computing.

[0360] M34. The method according to any of the preceding method embodiments with the features of M32, wherein at least one of the method's modules is configured for neuromorphic computing.

[0361] M35. The method according to any of the preceding method embodiments with the features of method embodiment M3, wherein operating the acquiring module comprises acquiring at least one data.

[0362] M36. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one data in real-time.

[0363] M37. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one image data.

[0364] M38. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one acoustic data.

[0365] M39. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one wind data. M40. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one meteorological data.

[0366] M41. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one atmospheric pressure data.

[0367] M42. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one rotation rate data wherein the rotation rate data comprises at least one data related to the rotation rate of the external and / or outer system.

[0368] M43. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one acceleration data wherein the acceleration data comprises at least one data related to the kinematic acceleration of the external and / or outer system.

[0369] M44. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one voltage data wherein the voltage data relates to the voltage of the external system and / or outer system.

[0370] M45. The method according to any of the preceding method embodiments with the features of method embodiments M7 and M35, wherein operating the acquiring module comprises acquiring at least one rotational speed data wherein the at least one rotational speed data relates to the at least one motor.

[0371] M46. The method according to any of the preceding method embodiments with the features of method embodiment M35, wherein operating the acquiring module comprises acquiring at least one humidity data.

[0372] M47. The method according to any of the preceding method embodiments with the features of method embodiment M3, wherein operating the acquiring module comprises operating at least one analog sensor.

[0373] M48. The method according to any of the preceding method embodiments with the features of method embodiment M3, wherein operating the acquiring module comprises operating at least one digital sensor.

[0374] M49. The method according to any of the preceding method embodiments with the features of method embodiments M3 and, M33 and / or M34, wherein operating the acquiring module comprises operating at least one neuromorphic sensor.

[0375] M50. The method according to any of the preceding method embodiments with the features of method embodiment M35 and any combination of method embodiments M47-M49 wherein operating the at least one sensor comprises acquiring the at least one data.

[0376] M51. The method according to any of the preceding method embodiments with the features of method embodiments M4 and, M35 and / or M50, wherein operating the acquiring module comprises outputting the at least one data to the processing module.

[0377] M52. The method according to any of the preceding method embodiments, with the features of method embodiment M4, wherein operating the processing module comprises processing at least one data into an at least one processed data.

[0378] M53. The method according to any of the preceding method embodiments with the features of method embodiment M52, wherein operating the processing module comprises processing the at least one data into at the at least one processed data wherein the processed data comprises at least one response data.

[0379] M54. The method according to any of the preceding method embodiments with the features of method embodiments A34, M50, and M52, in combination with M47 and / or M48 wherein operating the processing module comprises converting the at least one data into at least one neuromorphic data wherein the at least one neuromorphic data comprises the at least one data converted to be correctly read by a neuromorphic chip.

[0380] M55. The method according to any of the preceding method embodiments with the features of method embodiment M52, wherein operating the processing module comprises outputting the at least one processed data to the noise cancellation module.

[0381] M56. The method according to any of the preceding method embodiments with the features of method embodiment M52, wherein operating the processing module comprises outputting the at least one processed data to the discriminator module.

[0382] M57. The method according to any of the preceding method embodiments with the features of method embodiment M52, wherein operating the processing module comprises outputting the at least one processed data to the collision avoidance module.

[0383] M58. The method according to any of the preceding method embodiments, with the features of embodiment M2, wherein operating the noise cancellation module comprises compensating for acoustic noise generated by the external and / or outer system.

[0384] M59. The method according to any of the preceding method embodiments, with the features of embodiment M2, wherein operating the noise cancellation module comprises compensating for acoustic noise generated by the external and / or outer system according to at least one data.

[0385] M60. The method according to any of the preceding method embodiments with the features of method embodiments M52, and M59, wherein operating the noise cancellation module comprises compensating for acoustic noise generated by the external and / or outer system according to at least one processed data.

[0386] M61. The method according to any of the preceding method embodiment with the features of method embodiment M59, wherein the at least one data comprises at least one acoustic data.

[0387] M62. The method according to any of the preceding method embodiments with the features of embodiment M2, wherein operating the noise cancellation module comprises performing acoustic noise cancellation.

[0388] M63. The method according to any of the preceding method embodiments with the features of method embodiments M59 and M62, wherein active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective is comprised outside of either the method or the external and / or outer system.

[0389] M64. The method according to any of the preceding method embodiments with the features of embodiment M2, wherein operating the noise cancellation module comprises performing sectorial noise cancellation.

[0390] M65. The method according to any of the preceding method embodiments, with the features of embodiment M2, wherein operating the noise cancellation module comprises performing sectorial active noise cancellation.

[0391] M66. The method according to any of the preceding method embodiments with the features of method embodimentsM59 and M65, wherein sectorial active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective in comprised outside of either the method or the external and / or outer system wherein operating the outside perspective is comprised in a predetermined positional sector.

[0392] M67. The method according to any of the preceding method embodiments with the features of method embodiments M59 and, M62 and / or M65 wherein operating the noise cancellation module comprises reducing acoustic noise depicted in the at least one data by at least lOdB by making use of active noise cancellation and / or sectorial active noise cancellation.

[0393] M68. The method according to any of the preceding method embodiments with the features of method embodiments M59 and, M63 and / or M66, wherein operating the noise cancellation module comprises reducing the acoustic noise depicted in the at least one primary acoustic data by at least lOdB according to the outside perspective.

[0394] M69. The method according to any of the preceding method embodiments with the features of embodiment M2, wherein operating the noise cancellation module comprises outputting at least one noise cancellation command, wherein operating the at least one noise cancellation command comprises compensating for acoustic noise generated by the external and / or outer system.

[0395] M70. The method according to any of the preceding method embodiments with the features of method embodiment M69, wherein the at least one noise cancellation command is adjustable.

[0396] M71. The method according to any of the preceding method embodiments with the features of method embodiment M70, wherein the at least one noise cancellation command is adaptively adjustable according to the external and / or outer system. M72. The method according to any of the preceding method embodiments with the features of method embodiment M61, wherein the at least one noise cancellation command is configured according to the content, the magnitude frequency response and / or phase response of the at least one acoustic data.

[0397] M73. The method according to any of the preceding method embodiments with the features of method embodiment M61, wherein operating the noise cancellation module comprises filtering out wind noise and / or aeroacoustic effects from the at least one acoustic data.

[0398] M74. The method according to any of the preceding method embodiments with the features of method embodiment M73, wherein operating the noise cancellation module comprises compensating for acoustic noise generated by the external and / or outer system according to at least one acoustic data, wherein the at least one acoustic data has been filtered out to remove wind noise and / or aeroacoustic effects.

[0399] M75. The method according to any of the preceding method embodiments with the features of method embodiment M62, wherein operating the noise cancellation module comprises generating at least one counter-noise signal.

[0400] M76. The method according to any of the preceding method embodiments with the features of method embodiments M59 and M75, wherein operating the noise cancellation module comprises generating at least one counter-noise signal according to the at least one data.

[0401] M77. The method according to any of the preceding method embodiments with the features of method embodiment M75, wherein operating the noise cancellation module comprises generating the at least one counter-noise signal according to at least one feedforward counter-noise generating algorithm.

[0402] M78. The method according to any of the preceding method embodiments with the features of method embodiment M75, wherein operating the noise cancellation module comprises generating the at least one counter-noise signal according to at least one feedback counter-noise generating algorithm.

[0403] M79. The method according to any of the preceding method embodiments with the features of method embodiment M75, wherein operating the noise cancellation module comprises generating the at least one counter-noise signal according to at least one hybrid counter-noise generating algorithm.

[0404] M80. The method according to any of the preceding method embodiments with the features of method embodiment M62, wherein operating the noise cancellation module comprises adaptively generating at least one counter-noise signal.

[0405] M81. The method according to any of the preceding method embodiments with the features of method embodiment M62, wherein operating the noise cancellation module comprises generating at least one counter-noise signal according to at least one Al algorithm.

[0406] M82. The method according to any of the preceding method embodiments with the features of embodiment M2, wherein operating the noise cancelling module comprises predicting acoustic noise to be generated by the external and / or outer system.

[0407] M83. The method according to any of the preceding method embodiments with the features of method embodiments M62 and M82 wherein operating the noise cancellation module comprises performing acoustic noise cancellation according to the prediction of the acoustic noise generated by the external and / or outer system.

[0408] M84. The method according to any of the preceding method embodiments with the features of method embodiment M82, wherein the prediction is generated by at least one Al algorithm.

[0409] M85. The method according to any of the preceding method embodiments, with the features of embodiment M2, wherein operating the noise cancellation module comprises setting up its parameters according to at least one Al algorithm.

[0410] M86. The method according to any of the preceding method embodiments, with the features of method embodiments M58 or M59, and M85, wherein operating the noise cancellation module comprises setting up its parameters for the compensation of acoustic noise according to at least one Al algorithm.

[0411] M87. The method according to any of the preceding method embodiments wherein the method comprises operating at least one actuator.

[0412] M88. The method according to any of the preceding method embodiments with the features of method embodiment M87, wherein operating the at least one actuator comprises operating at least one loudspeaker.

[0413] M89. The method according to any of the preceding method embodiments with the features of method embodiment M87, wherein operating the at least one actuator comprises operating at least one motor.

[0414] M90. The method according to any of the preceding method embodiments with the features of method embodiment M89, wherein operating the at least one external and / or outer system comprises operating the at least one motor.

[0415] M91. The method according to any of the preceding method embodiments with the features of method embodiments M2 and M87, wherein operating the noise cancelling module comprises operating at least one actuator.

[0416] M92. The method according to any of the preceding method embodiments with the features of method embodiments M91 wherein operating the at least one actuator comprises performing active noise cancellation and / or sectorial active noise cancellation.

[0417] M93. The method according to any of the preceding method embodiments with the features of method embodiments M91 wherein operating the at least one actuator comprises performing active noise cancellation and / or sectorial active noise cancellation according to at least one noise cancellation command.

[0418] M94. The method according to any of the preceding method embodiments with the features of method embodiments M69 and M91 wherein operating the at least one actuator comprises performing active noise cancellation and / or sectorial active noise cancellation according to the at least one noise cancellation command.

[0419] M95. The method according to any of the preceding method embodiments with the features of method embodiments M63 and / or M66, and M91 wherein operating the at least one actuator comprises performing active noise cancellation and / or sectorial active noise cancellation according to the outside perspective.

[0420] M96. The method according to any of the preceding method embodiments with the features of method embodiment M87, wherein operating the at least one actuator comprises outputting at least one acoustic signal. M97. The method according to any of the preceding method embodiments with the features of method embodiments M3 and M87, wherein operating the acquiring module comprises detecting at least one acoustic signal and / or at least one reflected acoustic signal outputted by the at least one actuator.

[0421] M98. The method according to any of the preceding method embodiments with the features of method embodiments M2 and M87, wherein operating the at least one actuator comprises calibrating the at least one actuator according to the noise cancellation module.

[0422] M99. The method according to any of the preceding method embodiments with the features of method embodiment M87, wherein operating the at least one actuator comprises calibrating the at least one actuator according to the localization module.

[0423] M100. The method according to any of the preceding method embodiments with the features of method embodiments M5 and M87, wherein operating the at least one actuator comprises calibrating the at least one actuator according to the collision avoidance module.

[0424] M101. The method according to any of the preceding method embodiments wherein operating the discriminator module comprises differentiating acoustic noise generated by at least one type of system from other acoustic noises.

[0425] M102. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein operating the discriminator module comprises differentiating acoustic noise generated by at least one type of system from other acoustic noises according to at least one acoustic data.

[0426] M103. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises at least one motor.

[0427] M104. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises at least one vehicle.

[0428] M105. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises an aircraft and / or an aircraft system.

[0429] M106. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises a watercraft and / or a watercraft system.

[0430] M107. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises at least one propeller and / or a propeller system.

[0431] M108. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises an airship.

[0432] M109. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises a ship.

[0433] Ml 10. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises a drone.

[0434] Mill. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises a vessel.

[0435] Ml 12. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises a multicopter drone.

[0436] Ml 13. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein the at least one type of system comprises a submarine.

[0437] Ml 14. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein operating the discriminator module comprises outputting at least one primary acoustic data wherein the at least one primary acoustic data relates to at least one acoustic noise generated by the at least one type of system. M115. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein operating the discriminator module comprises outputting at least one secondary acoustic data, wherein the at least one secondary acoustic data relates to other acoustic noises, wherein other acoustic noises relate to at least one acoustic noise not generated by the at least one type of system.

[0438] Ml 16. The method according to any of the preceding method embodiments with the features of method embodiment M114, wherein operating the discriminator module comprises outputting the at least one primary acoustic data to the localization module.

[0439] Ml 17. The method according to any of the preceding method embodiments with the features of method embodiment M116, wherein operating the discriminator module comprises outputting the at least one secondary acoustic data to the collision avoidance module.

[0440] M118. The method according to any of the preceding method embodiments wherein operating the localization module comprises differentiating acoustic noise generated by the external and / or outer system from at least one 3rdparty system.

[0441] M119. The method according to any of the preceding method embodiments wherein operating the localization module comprises locating the external and / or outer system and / or at least one 3rdparty system.

[0442] M120. The method according to any of the preceding method embodiments with the features of method embodiment M119, wherein operating the localization module comprises locating the external and / or outer system and / or at least one 3rdparty system with respect to each other.

[0443] M121. The method according to any of the preceding method embodiments with the features of method embodiment M119, wherein operating the localization module comprises locating the external and / or outer system and / or at least one 3rdparty system with respect to at least one data.

[0444] M122. The method according to any of the preceding method embodiments with the features of method embodiment M119, wherein operating the localization module comprises locating the external and / or outer system and / or at least one 3rdparty system according to at least one data.

[0445] M123. The method according to any of the preceding method embodiments with the features of embodiments M121 and / or M122, wherein the at least one data comprises at least one data related to at least one data according to any of method embodiments M37-M46.

[0446] M124. The method according to any of the preceding method embodiments with the features of method embodiment M118, wherein operating the localization module comprises differentiating acoustic noise generated by the external and / or outer system from at least one 3rdparty system according to at least one data.

[0447] M125. The method according to any of the preceding method embodiments with the features of any of method embodiments M118-M124, wherein the at least one 3rdparty system is configured as at least one type of system according to at least one of any of the preceding method embodiments M103-M113.

[0448] M126. The method according to any of the preceding method embodiments with the features of method embodiments M122 and / or M124 wherein the at least one data comprises at least one acoustic data.

[0449] M127. The method according to any of the preceding method embodiments with the features of method embodiment M126, wherein the at least one acoustic data comprises at least one acoustic noise data.

[0450] M128. The method according to any of the preceding method embodiments with the features of method embodiments M116 and, M122 and / or M124 wherein the at least one data comprises the at least one primary acoustic data.

[0451] M129. The method according to any of the preceding method embodiments with the features of method embodiment M118, wherein operating the localization module comprises outputting at least one 3rdparty system acoustic data wherein the at least one 3rdparty system acoustic data relates to acoustic noise generated by the at least one 3rdparty system.

[0452] M130. The method according to any of the preceding method embodiments with the features of method embodiment M118, wherein operating the localization module comprises outputting at least one self-system acoustic data wherein the at least one self-system acoustic data relates to acoustic noise generated by the external system and / or the outer system.

[0453] M131. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein operating the localization module comprises transmitting and / or receiving at least one data to and / or from the collision avoidance module.

[0454] M132. The method according to any of the preceding method embodiments, wherein operating the localization module comprises outputting at least one data to the external and / or outer system.

[0455] M133. The method according to any of the preceding method embodiments, wherein operating the localization module comprises outputting at least one data to an external device / server / service independent of the method according to any of the preceding method embodiments.

[0456] M134. The method according to any of the preceding method embodiments with the features of method embodiments M63 and / or M66, wherein operating the localization module comprises outputting at least one location data to the noise cancellation module, wherein operating the at last one location data relates to the outside perspective.

[0457] M135. The method according to any of the preceding method embodiments with the features of method embodiments M5, M121, M122, wherein operating the localization module comprises receiving the at least one data from the collision avoidance module.

[0458] M136. The method according to any of the preceding method embodiments with the features of method embodiment M119, wherein operating the localization module comprises improving the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0459] M137. The method according to any of the preceding method embodiments wherein operating the localization module comprises operating a 3rdparty localization module.

[0460] M138. The method according to any of the preceding method embodiments with the features of method embodiment M137, wherein operating the 3rdparty localization module comprises locating at least one 3rdparty system based on method embodiment M125 wherein the at least one 3rdparty system does not comprise the external system and / or outer system.

[0461] M139. The method according to any of the preceding method embodiments with the features of method embodiments M129 and M138, wherein operating the 3rdparty localization module comprises locating at least one 3rdparty system according to the at least one 3rdparty system acoustic data.

[0462] M140. The method according to any of the preceding method embodiments with the features of method embodiments M87 and M137, wherein operating the 3rdparty localization module comprises outputting at least one operation command to the at least one actuator.

[0463] M141. The method according to any of the preceding method embodiments with the features of method embodiment M140, wherein the at least one operational command comprises at least one command configured to output at least one acoustic signal.

[0464] M142. The method according to any of the preceding method embodiments with the features of method embodiments M51, M57, M97 and M141, wherein operating the 3rdparty localization module comprises detecting at least one 3rdparty system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0465] M143. The method according to any of the preceding method embodiments with the features of method embodiments M51, M57, M97 and M141, wherein operating the 3rdparty localization module comprises detecting at least one 3rdparty system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0466] M144. The method according to any of the preceding method embodiments with the features of method embodiment M139, wherein operating the 3rdparty localization module comprises differentiating 3rdparty direct noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty direct noise data comprises at least one non-reflected acoustic noise generated by at least one 3rdparty system.

[0467] M145. The method according to any of the preceding method embodiments with the features of method embodiment M139, wherein operating the 3rdparty localization module comprises differentiating 3rdparty reflected noise data from the at least one 3rdparty system acoustic data, wherein 3rdparty reflected noise data comprises at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the at least one 3rdparty system.

[0468] M146. The method according to any of the preceding method embodiments with the features of method embodiment(s) M144 and / or M145, wherein operating the 3rdparty localization module comprises differentiating the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to metadata related to at least one data, wherein the at least one data results in the at least one 3rdparty system acoustic data.

[0469] M147. The method according to any of the preceding method embodiments with the features of any of method embodiments M50 and M146, wherein the at least one data was detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system.

[0470] M148. The method according to any of the preceding method embodiments with the features of method embodiment M146, wherein operating the 3rdparty localization module comprises extracting the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the angle the at least one data resulting in the at least one 3rdparty system acoustic data was detected at.

[0471] M149. The method according to any of the preceding method embodiments with the features of method embodiment M146, wherein operating the 3rdparty localization module comprises extracting the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the magnitude of the at least one data resulting in the at least one 3rdparty system acoustic data.

[0472] M150. The method according to any of the preceding method embodiments with the features of method embodiment M146, wherein operating the 3rdparty localization module comprises extracting the 3rdparty direct noise data and / or the 3rdparty reflected noise data according to the time of arrival the at least one data resulting in the at least one 3rdparty system acoustic data was detected at.

[0473] M151. The method according to any of the preceding method embodiments with the features of method embodiment M146 wherein operating the 3rdparty localization module comprises extracting the 3rdparty direct noise data and / or the 3rdparty reflected noise data by making use of at least one Al algorithm.

[0474] M152. The method according to any of the preceding method embodiments wherein operating the localization module comprises operating a self-localization module.

[0475] M153. The method according to any of the preceding method embodiments with the features of method embodiment M152, wherein operating the self-localization module comprises locating the external system and / or the outer system.

[0476] M154. The method according to any of the preceding method embodiments with the features of method embodiments M130 and M153, wherein operating the selflocalization module comprises locating the external system and / or the outer system according to the at least one self-system acoustic data.

[0477] M155. The method according to any of the preceding method embodiments with the features of method embodiments M87 and M153, wherein operating the selflocalization module comprises outputting at least one operation command to the at least one actuator.

[0478] M156. The method according to any of the preceding method embodiments with the features of method embodiment M155, wherein the at least one operational command comprises at least one command configured to output at least one acoustic signal.

[0479] M157. The method according to any of the preceding method embodiments with the features of method embodiments M97 and M156, wherein operating the selflocalization module comprises detecting the external and / or system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0480] M158. The method according to any of the preceding method embodiments with the features of method embodiments M51, M57, M97 and M156, wherein operating the self-localization module comprises detecting the external and / or outer system according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0481] M159. The method according to any of the preceding method embodiments with the features of method embodiment M154, wherein operating the self-localization module comprises extracting self-direct noise data from the at least one self- system acoustic data, wherein self-direct noise data comprises at least one nonreflected acoustic noise generated by the external and / or outer system.

[0482] M160. The method according to any of the preceding method embodiments with the features of method embodiment M154, wherein operating the self-localization module comprises extracting self-reflected noise data from the at least one selfsystem acoustic data, wherein self-reflected noise data comprises at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by the external and / or outer system.

[0483] M161. The method according to any of the preceding method embodiments with the features of method embodiment(s) M159 and / or M160, wherein operating the selflocalization module comprises extracting the self-direct noise data and / or the selfreflected noise data according to metadata related to at least one data, wherein the at least one data results in the at least one self-system acoustic data.

[0484] M162. The method according to any of the preceding method embodiments with the features of any of method embodiments M50 and M161, wherein the at least one data was detected with respect to the position of the at least one sensor and / or the position of the external and / or outer system.

[0485] M163. The method according to any of the preceding method embodiments with the features of method embodiment M161, wherein operating the self-localization module comprises extracting the self-direct noise data and / or the self-reflected noise data according to the angle the at least one data resulting in the at least one self-system acoustic data was detected at.

[0486] M164. The method according to any of the preceding method embodiments with the features of method embodiment M161, wherein operating the self-localization module comprises extracting the self-direct noise data and / or the self-reflected noise data according to the magnitude of the at least one data resulting in the at least one self-system acoustic data.

[0487] M165. The method according to any of the preceding method embodiments with the features of method embodiment M161, wherein operating the self-localization module comprises extracting the self-direct noise data and / or the self-reflected noise data according to the time of arrival the at least one data resulting in the at least one self-system acoustic data was detected at. M166. The method according to any of the preceding method embodiments with the features of method embodiment M161 wherein operating the self-localization module comprises extracting the self-direct noise data and / or the self-reflected noise data by making use of at least one Al algorithm.

[0488] M167. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein operating the collision avoidance module comprises outputting at least one navigation command to the external system and / or the outer system.

[0489] M168. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein operating the collision avoidance module comprises implementing at least one collision avoidance algorithm based on at least one data.

[0490] M169. The method according to any of the preceding method embodiments with the features of embodiment M168, wherein the at least one data comprises at least one data related to at least one data according to any of method embodiments M37-M46.

[0491] M170. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein operating the collision avoidance module comprises implementing at least one collision avoidance algorithm based on at least one acoustic data.

[0492] M171. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein the collision avoidance module comprises an object detection module.

[0493] M172. The method according to any of the preceding method embodiments with the features of method embodiment M171, wherein operating the at least one object detection module comprises detecting at least one obstacle.

[0494] M173. The method according to any of the preceding method embodiments with the features of method embodiment M172, wherein operating the at least one object detection module comprises detecting at least one obstacle according to at least one data.

[0495] M174. The method according to any of the preceding method embodiments with the features of embodiment M173, wherein the at least one data comprises at least one data related to at least one data according to any of method embodiments M37-M46.

[0496] M175. The method according to any of the preceding method embodiments with the features of method embodiments M87 and M172, wherein operating the at least one object detection module comprises outputting at least one operation command to the at least one actuator.

[0497] M176. The method according to any of the preceding method embodiments with the features of method embodiment M175, wherein the at least one operational command comprises at least one command to output at least one acoustic signal.

[0498] M177. The method according to any of the preceding method embodiments with the features of method embodiments M97 and M176, wherein operating the at least one object detection module comprises detecting at least one obstacle according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0499] M178. The method according to any of the preceding method embodiments with the features of method embodiments M51, M57, M97 and M176, wherein operating the at least one object detection module comprises detecting at least one obstacle according to the at least one acoustic signal and / or at least one reflected acoustic signal.

[0500] M179. The method according to any of the preceding method embodiments with the features of method embodiment M172, wherein operating the object detection module comprises improving the accuracy of the location of the obstacle by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0501] M180. The method according to any of the preceding method embodiments with the features of method embodiment Ml 17, wherein operating the object detection module comprises extracting obstacle-direct noise data from the at least one secondary acoustic data, wherein obstacle-direct noise data comprises at least one non-reflected acoustic noise generated by at least one obstacle / object.

[0502] M181. The method according to any of the preceding method embodiments with the features of method embodiment Ml 17, wherein operating the object detection module comprises extracting obstacle-reflected noise data from the at least one secondary acoustic data, wherein obstacle-reflected noise data comprises at least one acoustic noise reflected by a reflective surface and / or reflective layer and / or reflective particle, generated by at least one obstacle / object.

[0503] M182. The method according to any of the preceding method embodiments with the features of method embodiment(s) M180 and / or M181, wherein operating the object detection module comprises extracting the obstacle-direct noise data and / or the obstacle-reflected noise data according to metadata related to at least one data, wherein the at least one data results in at least part of the at least one secondary acoustic data.

[0504] M183. The method according to any of the preceding method embodiments with the features of any of method embodiments M50 and A182, wherein the at least one data was detected with respect to the position of the at least one sensor and / or the position of the obstacle / object.

[0505] M184. The method according to any of the preceding method embodiments with the features of method embodiment A182, wherein operating the object detection module comprises extracting the obstacle-direct noise data and / or the obstacle - reflected noise data according to the angle the at least one data resulting in in at least part of the at least one secondary acoustic data.

[0506] M185. The method according to any of the preceding method embodiments with the features of method embodiment M161, wherein operating the object detection module comprises extracting the obstacle-direct noise data and / or the obstacle- reflected noise data according to the magnitude of the at least one data resulting in at least part of the at least one secondary acoustic data.

[0507] M186. The method according to any of the preceding method embodiments with the features of method embodiment M161, wherein operating the object detection module comprises extracting the obstacle-direct noise data and / or the obstacle- reflected noise data according to the time of arrival the at least one data resulting in at least part of the at least one secondary acoustic data.

[0508] M187. The method according to any of the preceding method embodiments with the features of method embodiment M161 wherein operating the object detection module comprises extracting the obstacle-direct noise data and / or the obstacle- reflected noise data by making use of at least one Al algorithm. M188. The method according to any of the preceding method embodiments with the features of method embodiment M171, wherein the object detection module comprises an object movement prediction module.

[0509] M189. The method according to any of the preceding method embodiments with the features of method embodiment M188, wherein operating the object movement prediction module comprises predicting at least one movement of at least one object.

[0510] M190. The method according to any of the preceding method embodiments with the features of method embodiment M189, wherein operating the object movement prediction module comprises predicting at least one movement of at least one object according to at least one Al algorithm.

[0511] M191. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein the collision avoidance module comprises an area monitoring module.

[0512] M192. The method according to any of the preceding method embodiments with the features of method embodiment M191, wherein operating the area monitoring module comprises mapping at least one area.

[0513] M193. The method according to any of the preceding method embodiments with the features of method embodiment M192, wherein operating the area monitoring module comprises mapping at least one area according to at least one data.

[0514] M194. The method according to any of the preceding method embodiments with the features of embodiments M193, wherein the at least one data comprises at least one data related to at least one data according to any of method embodiments M37-M46.

[0515] M195. The method according to any of the preceding method embodiments with the features of method embodiment M193, wherein the at least one data comprises at least one image data.

[0516] M196. The method according to any of the preceding method embodiments with the features of method embodiment M191, wherein operating the area monitoring module comprises operating at least one Al module. M197. The method according to any of the preceding method embodiments with the features of method embodiment M196, wherein the at least one Al module comprises a neuromorphic neural network.

[0517] M198. The method according to any of the preceding method embodiments with the features of method embodiments M195 and M196 wherein operating the at least one Al module comprises mapping at least one area according to the at least one image data.

[0518] M199. The method according to any of the preceding method embodiments with the features of method embodiment M196, wherein operating the at least one AI- module comprises implementing at least one Tiny Object Detection algorithm.

[0519] M200. The method according to any of the preceding method embodiments with the features of method embodiment M191, wherein operating the area monitoring module comprises mapping at least one area according to at least one acoustic data.

[0520] M201. The method according to any of the preceding method embodiments with the features of method embodiment M191, wherein operating the area monitoring module comprises mapping at least one area according to at least one SLAM algorithm.

[0521] M202. The method according to any of the preceding method embodiments with the features of method embodiment M192, wherein operating the area monitoring module comprises mapping at least one area according to at least one data.

[0522] M203. The method according to any of the preceding method embodiments with the features of method embodiment M5, wherein the collision avoidance module comprises a navigation module.

[0523] M204. The method according to any of the preceding method embodiments with the features of method embodiment M203, wherein operating the navigation module comprises outputting at least one navigation command to the external and / or outer system.

[0524] M205. The method according to any of the preceding method embodiments, wherein the method comprises operating the modules operated by the method, according to any of the preceding embodiments, separately.

[0525] M206. The method according to any of the preceding method embodiments, with the features of method embodiment(s) M7, M89, or M103, wherein the method comprises implementing at least one vibration suppression technique to suppress the vibrations caused by the at least one motor.

[0526] M207. The method according to any of the preceding method embodiments, wherein the method comprises operating the modules operated by the method according to any of the preceding embodiments, independently of their location with respect to the external and / or outer system.

[0527] M208. The method according to any of the preceding method embodiments, wherein operating the modules of the method comprises transmitting and / or receiving at least one data.

[0528] M209. The method according to any of the preceding method embodiments with the features of any of method embodiments M143-M151, M158-M166, M178-M187, wherein the method comprises operating a noise reflection detection module, wherein operating the noise reflection detection module comprises performing the at least one function described in any of the cited method embodiments.

[0529] M210. The method according to any of the preceding method embodiments with the features of any of method embodiments M143-M151, M158-M166, M178-M187 or M209, wherein the method comprises matching at least one direct / original signal to at least one reflected signal according to at least one cross-correlation algorithm.

[0530] M211. The method according to any of the preceding method embodiments with the features of any of method embodiments M143-M151, M158-M166, M178-M187, or M209, wherein the method comprises matching at least one direct / original signal to at least one reflected signal according to at least one matched filter.

[0531] M212. The method according to any of the preceding method embodiments with the features of any of method embodiments M143-M151, M158-M166, M178-M187, or M209, wherein the method comprises detecting at least one direct / original signal and / or at least one reflected signal according to at least one blind source separation algorithm. M213. The method according to any of the preceding method embodiments with the features of any of method embodiments M143-M151, M158-M166, M178-M187, or M209, wherein the method comprises differentiating between at least one direct / original signal and / or at least one reflected signal according to at least one time frequency analysis algorithm.

[0532] M214. The method according to any of the preceding method embodiments with the features of any of method embodiments M143-M151, M158-M166, M178-M187, or M209, wherein the method comprises differentiating between at least one direct / original signal and / or at least one reflected signal according to at least one matching pursuit algorithm.

[0533] M215. The method according to any of the preceding method embodiments, wherein operating the method comprises differentiating between at least one direct / original signal and / or at least one refracted signal.

[0534] M216. The method according to any of the preceding method embodiments with the features of method embodiment M215, wherein the method comprises differentiating between at least one direct / original signal and / or at least one refracted signal by making use of the method as described in any of method embodiments M143-M151, M158-M166, M178-M187 replacing the reflected signal with a refracted signal.

[0535] M217. The method according to any of the preceding method embodiments with the features of method embodiment(s) M138 and / or M153, the method comprises improving the accuracy of the location of the external and / or outer system and / or at least one 3rdparty system by making use of a change-point detection algorithm, a break-point detection algorithm, and / or an outlier detection algorithm.

[0536] M218. The method according to any of the preceding method embodiments with the features of method embodiment M101, wherein operating the discriminator module comprises storing acoustic noise generated by at least one type of system and / or at least one obstacle.

[0537] M219. The method according to any of the preceding method embodiments with the features of method embodiment M96, wherein operating the at least one actuator comprises outputting at least one acoustic signal, wherein the at least one acoustic signal corresponds to at least one acoustic noise generated by at least one type of system and / or at least one obstacle. M220. The method according to any of the preceding embodiments, with the features of any of method embodiments M218-M219, wherein the at least one type of system correspond to at least one of any type of system mentioned in preceding apparatus embodiments M103-M113.

[0538] M221. The method according to any of the preceding embodiments with the features of method embodiment M96, wherein the method comprises modifying the at least one acoustic signal.

[0539] Brief Figure Description

[0540] The present invention will now be described with reference to the accompanying drawings which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.

[0541] Fig. 1 schematically depicts an example of the apparatus according to embodiments of the present invention;

[0542] Fig. 2 schematically depicts an example of part of the apparatus according to embodiments of the present invention;

[0543] Fig. 3 schematically depicts an example of another part of the apparatus according to embodiments of the present invention;

[0544] Fig. 4 schematically depicts an example of the noise cancellation module according to embodiments of the present invention;

[0545] Fig. 5 schematically depicts an example of a further part of the invention according to embodiments of the present invention;

[0546] Fig. 6 schematically depicts an example of an application of the apparatus according to embodiments of the present invention.

[0547] It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings.

[0548] Detailed Figure Description

[0549] Fig. 1 schematically depicts an example of the apparatus 1000 according to embodiments of the present invention. The acquiring module 1100 may acquire data which may be transmitted to the processing module 1300. The data, after being processed may be sent from processing module 1300 to noise cancellation module 1200, discriminator module 1400 or collision avoidance module 1600.

[0550] The data, more particularly acoustic data, may be sent to noise cancellation module 1200, wherein noise cancellation module 1200 may be configured to perform noise cancellation according to the acoustic data. Even more particularly, it may perform active noise cancellation by making use of actuators 1700. Actuators 1700 would be configured to generate at least one counter-noise signal.

[0551] The data sent by the processing module 1300 may be sent to the discrimination module 1400, wherein discrimination module 1400 may be configured to distinguish between noise made by at least one type of system from other acoustic noise. The acoustic data related to the at least one type of system may be sent to localization module 1500. The acoustic data related to the other acoustic sources may be sent to the object detection module 1630.

[0552] The acoustic data related to the at least one type of system may be sent to the localization module 1500. The localization module 1500 may be configured to locate the external and / or outer system by making use of the self-localization module 1560 according to the data. The localization module 1500 may also be configured to locate a 3rdparty system by making use of the 3rdparty system localization module 1530 according to the data. The location data outputted by the localization module 1500 may be outputted to the collision avoidance module 1600.

[0553] The collision avoidance module may comprise the object detection module 1630, the area monitoring module 1660 and the navigation module 1690. The object detection module 1630 may be configured to detect at least one obstacle in the path of at least one part of the external and / or outer system. The area monitoring module 1660 may be configured to map at least one area. The navigation module 1690 may be configured to output at least one navigation command to the external and / or outer system.

[0554] The collision avoidance module 1600 and the modules comprised in it may perform their functions based on data received from the processing module 1300, the discriminator module 1400 and the localization module 1500. Additionally, the collision avoidance module 1600 may be configured to transmit data to the localization module 1500 such that the data sent from the collision avoidance module 1600, may enhance the localization module 1500's location data accuracy. Furthermore, the object detection module 1630 may output an operational command to the actuators 1700 wherein the operation command may comprise outputting an acoustic signal with the goal of performing object detection.

[0555] Fig. 2 schematically depicts an example of part of the apparatus 1000 according to embodiments of the present invention. More particularly, Fig. 2 highlights a feedback loop created by the connections between the modules of the invention. The modules may include the acquiring module 1100, the processing module 1300, the discriminator module 1400, the localization module 1500 and the object detection module 1630.

[0556] The object detection module 1630 and / or the localization module 1500 may output an operational command to the actuators 1700 wherein the operation command may comprise outputting an acoustic signal. Said acoustic signal may be sent out and reflected by an outside object (process not shown in this figure). The acquiring module 1100 may detect the reflected acoustic signal which may be processed by the processing module 1300. The processed acoustic signal may be transmitted to the object detection module 1630 or the discriminator module 1400, wherein the latter may transmit the processed signal to the localization module 1500.

[0557] The processing of the data by the previously mentioned modules, allows the apparatus 1000 to detect the position of an object via the reflected signal. Said object may comprise an obstacle, a 3rdparty system and / or the external and / or outer system.

[0558] This loop may be used to either obtain a more accurate position of the detected object, or may be used to detect the object's movement overtime. The object's movement may also be predicted by the object detection module 1630.

[0559] Fig. 3 schematically depicts an example of part of the apparatus 1000 according to embodiments of the present invention. More particularly, Fig. 3 highlights another function of the present invention created by the connections between the modules of the invention. The processing module 1300 is configured to output at least one processed data to the discriminator module 1400 and the noise cancellation module 1200.

[0560] The discriminator module 1400 outputs data to the localization modulel500, which in turns outputs data to the noise cancellation module 1200. The data may be used to determine the sector, or the outside perspective where the sectorial noise cancellation may need to take place. By continuously receiving data from the acquiring module (not in the figure), it is possible for the apparatus to update the target I target area of the sectorial active noise cancellation over time according to embodiments of the present invention. Fig. 4 schematically depicts an example of the noise cancellation module 1200 according to embodiments of the present invention. A noise signal 1390 may have been detected by the acquiring module 1100 and / or processed by the processing module 1300 (not in the current figure). A counter-noise generating block 1250 may be configured to generate a counter-noise signal 1290 according to the noise signal 1390.

[0561] Multiple algorithms may be comprised in the counter-noise generating block 1250, such as feedforward counter-noise generating algorithm 1253, feedback counter-noise generating algorithm 1256, and / or hybrid counter-noise generating algorithm 1259. These algorithms may be used separately or together to be able to generate counter-noise signal 1290 according to noise signal 1390.

[0562] Parameters 1230 are configured to adjust the counter-noise generating block 1250 and its algorithms. These parameters may be adjusted via an Al-algorithm 1235 wherein the AI- algorithm 1235 may be configured to update the parameters 1230 such according to the noise signal 1390 and the counter-noise generating block 1250. The Al-algorithm 1235 may also be configured to update parameters 1230 according to the counter-noise signal generated.

[0563] Fig. 5 schematically depicts an example of a part of the invention 1000 according to embodiments of the present invention. More particularly, Fig. 5 depicts the localization module 1500 and the collision avoidance module 1600.

[0564] The 3rdparty localization module 1530 may be configured to output data to the object detection module 1630. The self-localization module 1560 may be configured to output data to the navigation module 1690. The object detection module 1630 may be configured to output data to the area monitoring module 1660 and the navigation module 1690.

[0565] The data transferred is configured to enhance the accuracy and / or performance of the output of each algorithm performed by each separate module. For example, the area monitoring module 1660 may be configured to output at least one map data to the localization module 1500 allowing to module to generate at least one location with respect to the at least one map data and / or according to the at least one map data.

[0566] Another example may be the object detection module 1630 and the area monitoring module 1660 transmitting data to the navigation module 1690 such that the navigation module 1690 may generate at least one navigation command according to the at least one obstacle detected and at least one map data. Fig. 6 schematically depicts an example of at least one application of the apparatus 1000 according to embodiments of the present invention. More particularly, the acquiring module 1100 may be configured to detect direct acoustic signals and / or reflected acoustic signals generated by a 3rdparty system / obstacle 2000. The direct acoustic signals and / or reflected acoustic signals may also be generated by an obstacle (not in figure). The reflected acoustic signals may be generated by the reflection of at least one direct acoustic signal onto at least one reflective surface / layer 3000. The acquired signal would then be processed and propagated through the apparatus according to embodiments of the present invention.

[0567] While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims.

[0568] Whenever a relative term, such as "about", "substantially" or "approximately" is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., "substantially straight" should be construed to also include "(exactly) straight".

[0569] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z). Corresponding considerations apply when terms like "after" or "before" are used. Reference Numerals

[0570] 1000 Apparatus

[0571] 1100 Acquiring module

[0572] 1200 Noise cancellation module

[0573] 1230 Noise cancellation module parameters

[0574] 1235 Al algorithm

[0575] 1250 Counter-noise generating algorithm

[0576] 1253 Feedforward counter-noise generating algorithm

[0577] 1256 Feedback counter-noise generating algorithm

[0578] 1259 Hybrid counter-noise generating algorithm

[0579] 1290 Counter-noise signal

[0580] 1300 Processing module

[0581] 1390 Noise signal

[0582] 1400 Discriminator module

[0583] 1500 Localization module

[0584] 1530 3rdparty localization module

[0585] 1560 Self-localization module

[0586] 1600 Collision avoidance module

[0587] 1630 Object detection module

[0588] 1660 Area monitoring module

[0589] 1690 Navigation module

[0590] 1700 Actuator(s)

[0591] 2000 3rdparty system / obstacle

[0592] 3000 Reflective surface

Claims

Claims1. An apparatus for an external system and / or an outer system, wherein the apparatus comprises, a discriminator module, wherein the discriminator module is configured to differentiate acoustic noise generated by at least one type of system from other acoustic noises; and a localization module, wherein the localization module is configured to differentiate acoustic noise generated by the external and / or outer system from at least one 3rdparty system and / or locate the external and / or outer system and / or at least one 3rdparty system according to at least one data, wherein the at least one data comprises at least one acoustic data, and wherein the at least one acoustic data comprises at least one acoustic noise data; wherein the external system is a system manufactured without the apparatus and the outer system is a system manufactured with the apparatus, and wherein the external system and / or the outer system comprise at least one motor.

2. The apparatus according to the preceding claim, wherein the apparatus comprises a noise cancellation module, wherein the noise cancellation module is configured to compensate for acoustic noise generated by the external and / or outer system.

3. The apparatus according to any of the preceding claims with the features of claim 2, wherein the noise cancellation module is configured to perform acoustic noise cancellation, wherein active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one processed data with respect to an outside perspective, wherein the outside perspective is comprised outside of either the apparatus or the external and / or outer system.

4. The apparatus according to any of the preceding claims with the features of claim 2, wherein the noise cancellation module is configured to perform sectorial active noise cancellation, wherein sectorial active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective in comprised outside of either the apparatus or the external and / or outer system wherein the outside perspective is comprised in a predetermined positional sector.

5. The apparatus according to any of the preceding claims, wherein the discriminator module is configured to output at least one primary acoustic data wherein the at least one primary acoustic data relates to at least one acoustic noise generated by the at least one type of system, and / or wherein the discriminator module isconfigured to output at least one secondary acoustic data, wherein the at least one secondary acoustic data relates to other acoustic noises, wherein other acoustic noises relate to at least one acoustic noise not generated by the at least one type of system.

6. The apparatus according to any of the preceding claims with the features of claim 4, wherein the localization module is configured to output at least one location data to the noise cancellation module, wherein the at last one location data relates to the outside perspective.

7. The apparatus according to any of the preceding claims wherein the apparatus comprises an acquiring module, wherein the acquiring module is configured to acquire the at least one data.

8. The apparatus according to any of the preceding claims wherein the apparatus comprises a collision avoidance module, wherein the collision avoidance module comprises an object detection module, wherein the at least one object detection module is configured to detect at least one obstacle, wherein the collision avoidance module comprises an area monitoring module, wherein the area monitoring module is configured to map at least one area according to at least one acoustic data, wherein the collision avoidance module comprises a navigation module, wherein the navigation module is configured to output at least one navigation command to the external and / or outer system.

9. The apparatus according to any of the preceding claims wherein the apparatus comprises at least one actuator, wherein the at least one actuator is configured to output at least one acoustic signal.

10. The apparatus according to any of the preceding claims, wherein the apparatus comprises a noise reflection detection module, wherein the noise reflection detection module is configured to detect at least one 3rdparty system and / or at least one obstacle and / or the external and / or outer system, according to the at least one acoustic signal and / or at least one reflected acoustic signal.

11. A method for an external system and / or an outer system, wherein the method comprises, operating a discriminator module, wherein operating the discriminator module comprises differentiating acoustic noise generated by at least one type of system from other acoustic noises; andoperating a localization module, wherein operating the localization module comprises differentiating acoustic noise generated by the external and / or outer system from at least one 3rdparty system and / or locating the external and / or outer system and / or at least one 3rdparty system, according to at least one data, wherein the at least one data comprises at least one acoustic data, and wherein the at least one acoustic data comprises at least one acoustic noise data; wherein the external system is a system manufactured without the modules operated by the method and the outer system is a system manufactured with the modules operated by the method, and wherein the external system and / or the outer system comprise at least one motor.

12. The method according to the preceding method claim, wherein the method comprises operating a noise cancellation module, wherein operating the noise cancellation module comprises compensating for acoustic noise generated by the external and / or outer system.

13. The method according to any of the preceding method claims with the features of claim 12, wherein operating the noise cancellation module comprises performing acoustic noise cancellation, wherein active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective is comprised outside of either the method or the external and / or outer system.

14. The method according to any of the preceding method claims with the features of claim 12, wherein operating the noise cancellation module comprises performing sectorial active noise cancellation, wherein sectorial active noise cancellation comprises actively cancelling the acoustic noise depicted in the at least one data with respect to an outside perspective, wherein the outside perspective in comprised outside of either the method or the external and / or outer system wherein operating the outside perspective is comprised in a predetermined positional sector.

15. The method according to any of the preceding method claims, wherein operating the discriminator module comprises outputting at least one primary acoustic data wherein the at least one primary acoustic data relates to at least one acoustic noise generated by the at least one type of system and / or wherein operating the discriminator module comprises outputting at least one secondary acoustic data, wherein the at least one secondary acoustic data relates to other acoustic noises, wherein other acoustic noises relate to at least one acoustic noise not generatedby the at least one type of system.

16. The method according to any of the preceding method claims with the features of claim 14, wherein operating the localization module comprises outputting at least one location data to the noise cancellation module, wherein operating the at last one location data relates to the outside perspective.

17. The method according to any of the preceding method claims wherein the method comprises operating an acquiring module, wherein operating the acquiring module comprises acquiring at least one data.

18. The method according to any of the preceding method claims wherein the method comprises operating a collision avoidance module, wherein the collision avoidance module comprises an object detection module, wherein operating the at least one object detection module comprises detecting at least one obstacle, wherein the collision avoidance module comprises an area monitoring module, wherein operating the area monitoring module comprises mapping at least one area according to at least one acoustic data, wherein the collision avoidance module comprises a navigation module, wherein operating the navigation module comprises outputting at least one navigation command to the external and / or outer system.

19. The method according to any of the preceding method claims wherein the method comprises operating at least one actuator, wherein operating the at least one actuator comprises outputting at least one acoustic signal.

20. The method according to any of the preceding method claims wherein the method comprises operating a noise reflection detection module, wherein operating the noise reflection detection module comprises detecting at least one 3rdparty system and / or at least one obstacle and / or the external and / or outer system, according to the at least one acoustic signal and / or at least one reflected acoustic signal.

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