143 results about "Active noise control" patented technology

The invention provides a self-adaptive collaborative acoustic environment active treatment system and method. The system comprises a multi-dimensional environment sensing network module, an intelligent voiceprint recognition and sound field prediction module, an active noise control module, a central intelligent collaborative regulation, diagnosis and self-learning module and a passive acoustic intervention module. The multi-dimensional environment sensing network module collects multi-dimensional sensing data in real time; the intelligent voiceprint recognition and sound field prediction module outputs a voiceprint recognition result and a noise source space coordinate and predicts a sound field evolution trend; the active noise control module outputs a residual noise signal; the central intelligent cooperative regulation, diagnosis and self-learning module outputs an active and passive cooperative control instruction set; the passive acoustic intervention module controls broadband noise to block or change its propagation path. According to the invention, through a dual-channel self-learning mechanism of the deviation diagnosis capability, continuous evolution of system performance can be realized according to a deviation autonomous optimization acoustic model and a control strategy, and accurate, efficient and prospective active treatment is carried out on a complex noise environment.

The embodiment of the invention relates to the technical field of fans, and particularly discloses an active noise reduction fan which comprises a shell which is a portable shell, a cavity is formed in the shell, and an air inlet and an air outlet which are communicated with each other are formed in the two ends of the shell respectively; the fan assembly is arranged in the cavity and used for rotating to generate wind pressure so that air can be sucked in from the air inlet, passes through the cavity and then is blown out from the air outlet; the noise reduction assembly is arranged in the cavity and used for collecting noise signals inside and outside the cavity in real time when the fan assembly operates and sending out noise reduction signals according to the noise signals; wherein the noise reduction signal is a dynamic comprehensive noise signal including air inlet noise, air outlet noise, fan blade noise and / or motor noise. Through reasonable design of functions of the noise reduction assembly, active noise control is performed on the noise signal by using the noise reduction signal, and the noise level during operation of the fan is reduced.

In certain aspects, an active noise control (ANC) method and system for a headphone are disclosed. It is determined whether a music signal is played by a speaker of the headphone. Responsive to the music signal not being played by the speaker and a noise level of an ambient noise signal being greater than a noise threshold, a set of noise feedforward (FF) signals is obtained based on a set of FF microphone signals acquired by a set of FF microphones of the headphone. A noise feedback (FB) signal is obtained based on a first FB microphone signal acquired by a FB microphone of the headphone. A set of leakage monitoring parameters is obtained based on the set of noise FF signals and the noise FB signal. A set of FF filter parameters for a set of FF filters is adjusted based on the set of leakage monitoring parameters.

The invention provides a robust online secondary path modeling active noise control method. The method comprises the following steps: S1, carrying out primary noise signal acquisition by using a microphone; s2, weight vector updating is carried out on the primary noise, and an error signal is obtained through calculation of the updated compensation signal and the main noise; s3, updating an adaptive filtering weight in secondary path modeling by using the error signal; and S4, enabling i to be equal to i + 1, and repeating the steps S1 to S3 until the noise is controlled. Compared with other robust cost functions, the cost function provided by the invention has better tolerance and smoothness, so that the cost function can show excellent performance in the aspect of suppressing pulse interference, and in addition, in consideration that the performance of the algorithm may be reduced when the secondary path changes, adaptive secondary path modeling is adopted to supplement the algorithm.

A system comprises an ear-worn electronic device configured to be worn by a wearer. The ear-worn electronic device comprises a processor and memory coupled to the processor. The memory is configured to store an annoying sound dictionary representative of a plurality of annoying sounds pre-identified by the wearer. A microphone is coupled to the processor and configured to monitor an acoustic environment of the wearer. A speaker or a receiver is coupled to the processor. The processor is configured to identify different background noises present in the acoustic environment, determine which of the background noises correspond to one or more of the plurality of annoying sounds, and attenuate the one or more annoying sounds in an output signal provided to the speaker or receiver.

The invention provides a control method and a control system for building an independent acoustic space of an automobile cabin, which are applied to the technical field of sound field control of the automobile cabin, and are used for inputting a multi-channel vibration signal acquired in real time into a deep learning noise prediction model to obtain an in-automobile background noise prediction value at a future moment; the prediction noise is filtered by the secondary path estimation model and then is used as a feedforward reference signal for active noise reduction, and the prediction noise and a residual signal collected by a bright area error microphone are jointly used for weight updating of an adaptive filter; the updated filter generates a reverse-phase noise elimination signal and the reverse-phase noise elimination signal is played by a secondary loudspeaker in real time so as to counteract background noise of the bright area; and closed-loop adjustment of the noise reduction effect is formed based on the sound pressure level contrast ratio of the bright and dark areas, so that the independent acoustic space is kept stable in the time-varying environment of vehicle driving. According to the method, the low noise of the bright area and the isolated sound field of the dark area in the vehicle are realized by fusing deep learning noise prediction and active noise control, and the personalized auditory experience in the vehicle can be remarkably improved.

The invention discloses an active noise control method and system for a new energy automobile, belongs to the technical field of automobile noise reduction, and is used for solving the problems that an existing active noise control technology is poor in stability, influences of road conditions on cavity noise are not considered, tire noise cannot be predicted and restrained in advance according to the front road conditions, and the noise is affected. And the noise reduction effect is not ideal. The method comprises the following steps: constructing a target data set based on noise signal features and road surface point cloud data; training a noise signal prediction model through the target data set; acquiring real-time point cloud data of a road surface in front of the vehicle, and inputting the real-time point cloud data into the noise signal prediction model to obtain noise signal prediction features; according to the noise signal prediction features, noise pre-adjustment signals are generated, and vehicle noise is preliminarily suppressed; and inputting the real-time noise signal obtained after preliminary suppression into an adaptive filter, and compensating the noise pre-adjusting signal to further suppress the vehicle noise.

An intelligent active noise control device according to an embodiment includes a microphone mounted in a vehicle and receiving in-vehicle noise, a noise of interest model configurer generating a noise of interest model by being trained on noise characteristics according to a preset type of noise, a similarity analysis and decision unit analyzing collected noise by comparing the collected noise with the noise model of interest, an active noise control area configurer setting an active noise control area according to an analyzed similarity, and an active noise control sound output unit outputting an active noise control sound to the set active noise control area.

The invention relates to an active noise reduction device, an active noise reduction method and a range hood, and the active noise reduction device comprises a sound signal collection device which is used for collecting a noise signal; the controller is electrically connected with the sound signal acquisition device, and the controller is configured to generate a noise reduction signal for counteracting the noise signal according to the noise signal acquired by the sound signal acquisition device; the sound signal transmitting device is electrically connected with the controller and is used for transmitting a noise reduction signal; the device is characterized in that the sound signal transmitting device comprises a plurality of plasma loudspeakers which are circumferentially arranged around the noise source, and the plasma loudspeakers are configured to be capable of generating electric arcs through high-voltage electricity so as to drive air to produce sound through the vibration of the electric arcs. The active noise reduction device can be suitable for a complex noise environment, is slightly influenced by the environment and is low in complexity; and the discharge parameters of the plasma loudspeaker can be obtained through a noise reduction method so as to meet the real-time requirement of a dynamic noise environment, and the method is suitable for active noise control, sound field regulation and control and other scenes.

The invention discloses a parameter self-adjusting robust active noise control method, and relates to the technical field of noise control. The method comprises the following steps: acquiring a sound pressure signal in real time, processing the sound pressure signal to form a discrete noise signal as a reference signal, and constructing a dynamically updated reference signal sequence; constructing a filter and generating a weight coefficient thereof, and filtering the reference signal to generate an output signal; processing an output signal of the filter through a secondary path to generate a de-noising signal, collecting a residual signal, feeding back the residual signal to the filter, providing error feedback for self-adaptive iteration of the filter, and driving a weight coefficient of the filter to be optimized and updated; determining an optimal parameter combination for the generalized robust adaptive loss function to realize adaptive optimal determination; and updating a filter weight coefficient vector. According to the method, through organic combination of a self-adaptive parameter adjustment mechanism and a robust control strategy, the fast convergence characteristic, the low steady-state error and the high impact resistance are achieved, and the noise suppression efficiency in the complex environment is remarkably improved.

The invention discloses a discontinuous Galerkin finite element and active noise reduction embedded fusion simulation system and method, and belongs to the field of ANC active noise reduction simulation. Comprising a DG-FEM acoustic solving module which is used for carrying out acoustic simulation calculation and comprises grid topology construction, boundary condition construction and a DG-FEM acoustic solver; the ANC control module is embedded in a bottom numerical framework of the DG-FEM acoustic solver and is used for realizing active noise control; the closed-loop control coupling interface is used for realizing real-time data interaction between the DG-FEM acoustic solving module and the ANC control module; according to the method, an ANC core algorithm is deeply embedded into a bottom layer numerical framework of a DG-FEM acoustic solver, and native integrated simulation of the ANC and the DG-FEM is achieved. According to the method, the dependence on external software coupling is eliminated, and the need of a user to write and compile complex custom subprograms or scripts is avoided, so that the acoustic simulation modeling process involving active noise control is remarkably simplified, and the technical threshold is greatly reduced.

The invention discloses a transmission path grading detection and reference sensor selection method based on in-vehicle active noise control, and belongs to the field of active noise control. The method comprises the steps that sensors are arranged at suspension rod pieces of a to-be-tested automobile, and microphones are arranged at seat headrests of the to-be-tested automobile; obtaining an error noise signal collected by the microphone and a reference vibration signal collected by the sensor when the to-be-tested automobile runs under the test working condition; a main control area and a reference vibration signal group are selected, and sensors corresponding to reference vibration signals which are not selected into the reference vibration signal group are removed; according to the method, sensors are arranged at wheel centers, a front auxiliary frame and a rear auxiliary frame of a to-be-detected automobile, error noise signals and reference vibration signals are obtained, L reference vibration signals with the highest multi-coherence are obtained through traversal optimization, and finally active noise control is conducted through a road noise active control system on the basis of the L reference vibration signals. According to the invention, the integrity and the feasibility of transmission path detection are improved, and the test cost is also saved.

An active noise control method, a storage medium, an electronic device, a system and a vehicle. The method comprises: acquiring multiple error signals and multiple reference signals at different positions; determining target step size data, wherein the target step size data comprises a convergence step size that corresponds to each reference signal and is from each secondary loudspeaker to each error microphone; determining a coefficient of an adaptive filter of each secondary loudspeaker on the basis of the error signals, the reference signals and the target step size data; and controlling an output signal of each secondary loudspeaker on the basis of the reference signals and the coefficient of the adaptive filter of each secondary loudspeaker.

The invention discloses a DL-Sig-FxNLMS algorithm-based active noise reduction system and method for a dual-feedforward vehicle, and belongs to the technical field of active noise control. The active noise reduction system structurally comprises a reference microphone, an auxiliary microphone, an error microphone, a loudspeaker, a DSP chip and a vehicle-mounted chip. The active noise reduction method comprises the steps that a microphone obtains sound signals, a mode is selected, a sound signal separation module works, a double-feedforward Sig-FxNLMS algorithm is executed, a vehicle-mounted chip executes CNN to update filter coefficients, and a loudspeaker outputs updated reverse sound waves. Through the DL-Sig-FxNLMS algorithm, effective separation and suppression of audio signals in a complex environment are realized. According to the method, the human voice and the environmental noise can be effectively separated and suppressed, so that the accuracy and the practicability of audio signal processing are remarkably improved.

The application relates to the field of active noise reduction technology, in particular to an ANC (active noise control) active noise reduction simulation system, which comprises a first pickup sensor, a second pickup sensor, a simulation module, a microprocessor and a mediation unit. External sound of an ANC earphone is collected by the first pickup sensor, then noise reduction processing is carried out by using a noise reduction controller, internal sound of the ANC earphone is collected by the second pickup sensor, and waveform of the internal sound of the ANC earphone is simulated by the simulation module, then the waveform is compared with a preset optimal waveform by the microprocessor, parameters of a noise reduction filter are adjusted by the mediation unit, and features of the simulated sound waveform are extracted conveniently, the noise reduction module of the noise reduction earphone is systematically and visually adjusted, and noise reduction performance difference between left and right earphones is minimized.

The invention discloses an active noise control method based on multichannel adaptive filtering, and relates to the technical field of automobile noise control and signal processing. The invention discloses an active noise control method based on multi-channel adaptive filtering. The method comprises the following steps: acquiring original noise signals at different positions in a vehicle through a reference microphone; performing multi-band band-pass filtering on the acquired noise signal to extract noise components of a target frequency band; inputting the filtered reference signal into a multi-channel adaptive filtering controller to generate an anti-noise signal; estimating a secondary path to generate an actual anti-noise signal based on the transmission path of the loudspeaker and the error microphone; through a feedback signal collected by an error microphone, closed-loop iteration is carried out to update and control a filter coefficient, and real-time offset and dynamic convergence of a noise signal are realized; through vector superposition of an original noise signal and an actual anti-noise signal, efficient and real-time suppression of multi-channel and broadband noise is realized. The method has more excellent multi-target noise reduction performance and robustness.

Techniques are described herein for classification-based selective pass-through for wearable audio components with active noise control (ANC). Embodiments receive ambient noise with a first artificial neural network pre-trained to classify attention-seeking audio (ASA) components present in the ambient noise. A conditional signal is output based at least on the classification. For example, the first pre-trained network generates an embedded space representation (ESR) characterizing the ASA component, and the conditional signal is based on the ESR. A second artificial neural network is pre-trained to generate a pass-through audio output based on the same ambient audio and the conditional signal, so that the pass-through audio output corresponds to the ASA component. The pass-through audio output is delayed past an ANC processing window so that essentially all the ambient noise except for the ASA component is cancelled by the ANC.

An electronic system with heat dissipation and feed-forward active noise control function includes a fan module, an embedded controller, a reference microphone, a multi-channel speaker module, a beamforming control module and an active noise reduction controller. The beamforming control module controls the orientation of the multi-channel speaker module. The multi-channel speaker module provides anti-phase noise signals to cancel noise according to a speaker control signal. The reference microphone detects broadband noise generated by the fan module in operation to provide a corresponding broadband noise signal. A virtual microphone module of the active noise reduction controller provides a virtual error signal according to a transfer function between the reference microphone and a physical error microphone, a transfer function between the multi-channel speaker module and the physical error microphone and the broadband noise signal, and generates the speaker control signal according to a synchronization signal, the broadband noise signal and the virtual error signal.

The invention provides an active noise control method, device and system, and the method comprises the steps: collecting a reference noise signal, and collecting an error noise signal; separating a feedforward error signal and a feedback error signal from the error noise signal based on a processing signal of a self-adaptive filter in the error separation system on the reference noise signal; the filtering parameter of the adaptive filter is updated based on the maximum correlation entropy between the processed signal and the noise residual signal after noise reduction of the reference noise signal. While the noise reduction effect is ensured, errors caused by coupling between the feedforward control system and the feedback control system are avoided, and the convergence effect of the system is effectively improved.

The application provides an underwater structure noise control method based on a parametric secondary sound source, which comprises the following steps: firstly, using the sound pressure and normal velocity information of a structure surface, a far-field noise prediction method is adopted to obtain the radiation noise at a virtual reference point in the far field, thereby providing an input basis for parametric secondary sound source control; secondly, a multi-channel parametric sound field model is established to verify the controllability of the parametric secondary sound field, and a regularization least square algorithm is adopted to obtain multi-channel signal emission parameters by taking the sum of the sound pressures at multiple virtual reference points in the far field as the minimum target; finally, when the monitoring information of the underwater structure is missing, a local control strategy is adopted to obtain the underwater structure noise control effect. The application is directed to directional control in the accurate area of far-field noise prediction, solves the problem of poor traditional active noise reduction effect caused by missing monitoring information, improves the reliability and stability of the system, and lays a theoretical foundation for the application of underwater structure active noise control technology.

A system comprises an ear-worn electronic device configured to be worn by a wearer. The ear-worn electronic device comprises a processor and memory coupled to the processor. The memory is configured to store an annoying sound dictionary representative of a plurality of annoying sounds pre-identified by the wearer. A microphone is coupled to the processor and configured to monitor an acoustic environment of the wearer. A speaker or a receiver is coupled to the processor. The processor is configured to identify different background noises present in the acoustic environment, determine which of the background noises correspond to one or more of the plurality of annoying sounds, and attenuate the one or more annoying sounds in an output signal provided to the speaker or receiver.

The invention discloses an in-vehicle active noise control method and system based on a convolutional recurrent network, and the method comprises the steps: obtaining reference signals collected by a sensor in real time, forming a cache data set by M reference signals, and enabling reference data in the cache data set to slide rightwards for a jump length after a short-time Fourier transform sliding window; the reference signals in the cache data set are subjected to short-time Fourier transform according to algorithm updating frequency, and one frame of time-frequency data is formed in each time of short-time Fourier transform; storing the N frames of time-frequency data by adopting a first-in first-out principle; and inputting the N frames of time-frequency data into the trained convolutional cyclic network model, and outputting an output signal for controlling a loudspeaker according to inverse Fourier transform. According to the method, the recurrent neural network is used for dynamically storing and updating time sequence steps, the noise reduction capability of the depth model is reserved, and the requirement for real-time noise reduction of the whole vehicle is met.

Methods of classifying and using acoustic filters for active noise control in hearing systems, such as headphones. The acoustic filters are determined and stored in a memory in the hearing system, making it therefore possible to quickly and efficiently select and use a certain filter in order to improve filter performance and filter stability in the hearing system.

The invention discloses a method and system for estimating active noise control performance of a vehicle. A method of estimating active noise control performance of a vehicle includes: performing active noise control by reproducing, via a speaker, an acoustic signal for reducing noise introduced into an interior of the vehicle from an exterior of the vehicle and receiving, from a microphone, a residual signal remaining after noise reduction; generating an estimation signal for a control signal for controlling an output of the speaker such that the acoustic signal is reproduced during the active noise control, generating an estimated signal for the original noise prior to the active noise control based on the residual signal and the estimated signal for the control signal, and estimating noise reduction performance based on the estimated signal for the original noise and the residual signal.

The invention provides an active noise reduction system which may be self-contained within a headset of the system, wherein circuitry within the system applies an active noise reduction profile to a signal to the speakers in the headset, to provide active noise reduction to the user of the headset, the system being arranged to receive power both from a power supply external to the system and from a battery of the system and to automatically modify the active noise reduction applied, in dependence on whether or not the system is connected to an external power supply.

In an active noise control method and system for a vehicle, the active noise control method for a vehicle includes receiving, by a head unit, a noise signal detected by at least one wireless earphone in a vehicle, generating, by an amplifier (AMP) controller, a target output signal corresponding to a sound to be provided to an occupant, generating, by an acoustic design processor (ADP) controller operatively connected to the amplifier (AMP) controller, an opposite phase signal to a signal made by subtracting the target output signal from the noise signal, and transmitting, by the head unit, the generated opposite phase signal to the at least one wireless earphone.

Machine learning network topologies, and training systems and methods therefor, are described for implementing unified sound conversion (USC), such as for automated name detection. Such automated name detection can support automated attention handling in wearable audio components with active noise control (ANC) to suppress ambient sound. Embodiments of USC network topologies include a feature generator comprising a convolutional encoder, an axial self-attention (ASA) block, and a deconvolutional decoder. A MEL converter generates input filter bank energies (FBEs) from an audio sample of a spoken word. The feature generator is trained to estimate output FBEs from the input FBEs, such that the output FBEs represent the linguistic information of the spoken word absent any speaker-specific suprasegmental features.

The present invention provides an active noise control device. The active noise control device (10) controls a speaker (18) to output a canceling sound in order to cancel out noise transmitted from a vibration source. The active noise control device comprises a control signal generating unit (68), a secondary path filter updating unit (84), and a feedback filter setting unit (23). The control signal generating unit (68) processes a reference signal corresponding to a specified frequency through a feedback filter and an adaptive notch filter as a decimation filter to generate a control signal for controlling the speaker (18); the secondary path filter updating unit (84) adaptively updates the secondary path filter in sequence; and the feedback filter setting unit (23) sets the feedback filter according to the secondary path filter. Thus, noise can be reduced even if the transmission characteristics change.

The invention belongs to the technical field of active noise control, and specifically relates to a method, device, equipment and medium for determining a controller of a feedback active noise reduction headphone. The present application obtains a secondary path data set of a target headphone, wherein the secondary path data set includes a plurality of primary data sets of different noise frequencies, each of the primary data sets includes a plurality of secondary data sets of different wearing methods, and each of the secondary data sets includes a plurality of secondary paths; constructs an initial controller of multiple control cores; determines the loss function of the initial controller according to the secondary path data set; optimizes each control core of the initial controller according to the loss function to obtain the most suitable controller for the target headphone. The present application uses the measured secondary path response to perform robust optimization of the feedback active noise control headphone, and gets rid of the problem of template design required in the traditional loop shaping method through the constraint design controller.

The invention discloses a multi-channel active noise control method capable of reducing the calculated amount. The method comprises the following steps: (1) building a multi-channel ANC system, wherein the system comprises M (Mgt; 1) the number of the microphones; (2) measuring a secondary channel transfer function; (3) selecting Q (Qlt; m) microphones, calculating a weighting matrix by using the latest T signals collected by the microphone system, and weighting the signals of the selected Q microphones to obtain a signal of a Q channel for controlling the updating of a filter; (4) updating a control filter; and (5) calculating the output of the control filter, and inputting the output into the secondary sound source for playing. According to the method, the Q microphones are randomly selected in each period of time, and the Q microphone signals are weighted to obtain the update signal of the control filter, so that the calculation amount can be greatly reduced, and the global noise reduction effect can be obtained.