30results about "Multi-channel direction finding" patented technology

The present invention introduces a method and system for monitoring an area based on acoustic data acquired from at least one acoustic sensor array. The proposed method initiates by acquiring (10) acoustic data and environmental data including at least temperature. This data is processed (20) to generate a global acoustic image by employing a delay and sum based beamformer for adjusting the phase and amplitude of the acoustic data. The method then determines (25) the presence of objects of interest within the monitored area based on the global acoustic image. Upon identifying an object of interest, a location identification procedure is initiated (30). This procedure includes estimating the initial location of the object then refining the acquired acoustic data focusing on sections which are estimated to pertain to the identified objects, thereby creating a locally refined acoustic image. Utilizing this refined image, the method generates a location data set which indicates the location of the object(s) of interest. Additionally, a categorization procedure (40) may be initiated subsequently to the location identification procedure (30), the categorization procedure (40) results in a categorization data set indicating characteristics of the identified object(s).

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

This application discloses a method for positioning a target audio signal by a computer device. The method includes: performing echo cancellation on the audio signals collected in a plurality of directions in a space, the audio signals comprising a target-audio direct signal; obtaining weights of a plurality of time-frequency points in the echo-canceled audio signals, a weight of each time-frequency point indicating a relative proportion of the target-audio direct signal in the echo-canceled audio signals at the time-frequency point; obtaining a weighted audio signal energy distribution of the audio signals in the plurality of directions by using the weights of the plurality of time-frequency points in the echo-canceled audio signals; and obtaining a sound source azimuth corresponding to the target-audio direct signal in the audio signals by using the weighted audio signal energy distribution of the audio signals in the plurality of directions.

Methods and systems for spatial filtering transmitters and receivers capable of simultaneous communication with one or more receivers and transmitters, respectively, the receivers capable of outputting source directions to humans or devices. The methods and systems use spherical wave field partial wave expansion (PWE) models for transmitted and received fields at antennas and for waves generated by contributing sources. The source PWE models have expansion coefficients expressed as functions of directional coordinates of the sources. For spatial filtering receivers a processor uses the output signals from at least one sensor outputting signals consistent with Nyquist criteria representative of the wave field and the source PWE model to determines directional coordinates of sources (wherein the number of floating point operations are reduced) and outputs the directional coordinates and communications to a reporter configured for reporting information to humans. For spatial filtering transmitters a processor uses known receiver directions and source partial wave expansions to generate signals for transducers producing a composite total wave field conveying communications to the specified receivers. The methods and communications reduce the processing required for transmitting and receiving spatially filtered communications.

A method involving receiving, by a control system, microphone signals obtained by a microphone array; enhancing, by the control system, directional components of the microphone signals, to produce directionally-enhanced microphone signals; calculating, by the control system, spatial covariance matrices based on the directionally- enhanced microphone signals; vectorizing, by the control system, the spatial covariance matrices to produce spatial covariance vectors; determining, by the control system, reference spatial covariance matrices of impulse responses for a range of directions; vectorizing, by the control system, the reference spatial covariance matrices to produce reference spatial covariance vectors; calculating, by the control system, similarities between the spatial covariance vectors and the reference spatial covariance vectors; and estimating, by the control system, one or more sound source directions of arrival based on the similarities.

The application discloses a kind of based on multi-array sparse channel estimation underwater acoustic environment perception method, underwater acoustic receiving end is received through multi-array array signal that is propagated through underwater acoustic multipath channel;The received signal of each array element is subjected to Hilbert transform, and analytic signal is obtained, and the cross-correlation function of analytic signal and transmitting signal is calculated;Based on cross-correlation function, using the orthogonal matching pursuit algorithm of combining constant false alarm detection dynamic threshold, the sparse channel parameters of each array element are estimated, including path amplitude and time delay;Utilize the sparse channel parameters of multi-array array, construct array response vector, estimate the angle of arrival of multipath signal, including direction angle and pitch angle by space spectrum peak value search;Based on the angle of arrival, amplitude and time delay of direct path and reflected path, the underwater environment structure is inverted by ray acoustics theory, including reflection point distance and reflection surface normal vector, realize the three-dimensional perception of underwater reflector.The application can improve multipath resolution, effectively reduce false alarm and miss detection.

The invention relates to a method for determining the arrival direction of signal paths on an array (2) of N sensors (3), comprising a first estimation, by sparse goniometry, of the vector γ satisfying the equation: where σ2 is the noise level, Rx is the vectorized covariance matrix of the signal x(t) considered at the output of the sensor array, and B is the calibration table of the array, and a deduction of the arrival directions of the paths based on this first estimation; then a second estimation of the vector γ satisfying with with composed of crossed terms, for arrival directions defined as a function of the deduced arrival directions, a second estimation of the arrival directions of the paths as a function of this second estimation of the vector γ. Figure for the abstract: Fig. 1

Provided is a sound direction detection sensor capable of detecting a direction from which sound is coming by using a multi-resonator array. The disclosed sound direction detection sensor includes two resonator arrays, each including a plurality of resonators having different resonance frequencies. The two resonator arrays have different directivities. Each resonator array serves as an audio sensor, and the sound direction detection sensor detects a direction from which sound is incident, regardless of a distance between audio sensors.

A system configured to improve sound source localization (SSL) processing by including distance information. By estimating a 3D location vector corresponding to a direction and a distance associated with a sound source, the system may extend SSL processing from 2D to 3D and generate a location estimate for the sound source. In addition, the system groups the 3D location vectors into 3D location cells having a regular structure (e.g., predetermined uniformity and / or symmetry), which simplifies SSL processing and results in a substantial reduction in computational cost. For example, the system may associate each 3D location cell with (i) location vectors contained within the 3D location cell, (ii) weight values indicating a relative weight of each location vector for the 3D location cell, and (iii) neighboring 3D location cells. Using these associations, the system may identify peak power value(s) corresponding to sound source(s) and generate location estimates for each sound source.

A conference speech presentation system, a sound localization method and apparatus, a conference system and a pickup device. The method includes the following steps: collecting (S101) a multi-channel voice signal through a directional microphone array; determining (S103) a steering vector including phase information and amplitude information according to array shape information and microphone pointing direction information; determining (S105) sound direction information according to the steering vector and the voice signal. By adopting this processing mode, both the phase information and the amplitude information are considered when determining the steering vector, which can effectively improve the accuracy of sound localization.

To provide simultaneous acoustic event detection across multiple assistant devices.SOLUTION: An implementation embodiment detects audio data that capture acoustic events at various assistant devices in an ecosystem, processes the audio data locally at each assistant device to generate a measure associated with the acoustic event using respective event detection models, processes the respective measures to determine whether the detected acoustic event is an actual acoustic event, and causes actions associated with the actual acoustic event to be performed in response to determining that the detected acoustic event is an actual acoustic event. In some implementation embodiments, the assistant device that has detected the audio data is anticipated to detect audio data that capture the actual acoustic event based on previous acoustic events being detected in each of the assistant devices.SELECTED DRAWING: Figure 4

An artificial intelligence (AI) apparatus including a memory and a processor configured to estimate a sound source localization based on at least one of image information, sound source information, and sensor information stored in the memory. The processor is configured to pre-process at least one of the image information, the sound source information, or the sensor information to generate test data, input the test data into a pre-trained AI model to estimate the sound source localization, calculate a sound source localization estimation evaluation score of the AI model for the test data, classify the test data into validation data based on the calculated sound source localization estimation evaluation score, change the AI model based on the classified validation data, and input the test data into the changed AI model to update the AI model.

This application provides an in-vehicle user positioning method, an in-vehicle interaction method, a vehicle-mounted apparatus, and a vehicle, applied to the field of autonomous driving. The in-vehicle user positioning method includes: obtaining a sound signal collected by an in-vehicle microphone; when a first voice command is recognized from the sound signal, determining a first user who sends the first voice command; and determining an in-vehicle location of the first user based on a mapping relationship between an in-vehicle user and an in-vehicle location. According to the solution of this application, a user who needs to interact can be determined according to a voice command, and then an in-vehicle location of the user who needs to interact can be determined based on the mapping relationship between an in-vehicle user and an in-vehicle location. Even if a plurality of users speak at the same time, the user who needs to interact can be located.

Techniques for determining the direction of arrival of an emergency event are described. Multiple audio sensors on a vehicle may receive audio data associated with the vehicle. A pair of audio sensors may be selected from the multiple audio sensors to generate audio data representative of sounds in the vehicle's environment. An angular spectrum associated with the pair of audio sensors may be determined based on the audio data. Features associated with the audio data may be determined based on the angular spectrum and / or the audio data itself. A direction of arrival (DoA) value associated with the sound may be determined based on the features using a machine learning model. A sound of the emergency event (e.g., a siren) may be detected in the audio data, and a direction associated with the emergency event related to the vehicle may be determined based on the features and the DoA value.

A signal localization device (1), comprising: a single sensor (4), a first redirection device (21) configured to guide an emitted signal (2) to the sensor (4) through a first channel, said first channel having a first intrinsic delay and a first spatial sensitivity so as to provide the emitted signal (2) to the sensor (4) with a first delay, a second redirection device (8) arranged to guide the emitted signal (2) to the sensor (4) through a second channel, said second channel having a second intrinsic delay and a second spatial sensitivity so as to provide the emitted signal (2) to the sensor (4) with a second delay, the first delay and the second delay being different, the second redirection (8) device comprising a delay structure (5) configured to provide the second delay to the emitted signal as a function of a direction (3) of the emitted signal (2), and a processing unit (6), configured to identify a localization of the emitted signal (2) as a function of the emitted signal, the first delay and the second delay.

To provide a system and a method for multi-source tracking and multi-stream speech section detection with reduced computational complexity by a microphone array to detect and process a target audio signal in a multi-stream audio environment.SOLUTION: A method: updates an audio track for multiple sound source audio signals, on the basis of TDOA detection data calculated for multiple sound source audio signals, by a steered minimal dispersion (STMV) beamformer based on TDOA trajectory information constructed in a multidimensional space defined by pairs of microphones from a microphone array; constructs speech interval detection (VAD) data for each of multiple audio tracks on the basis of TDOA detection data; and generates one or more emphasized multiple sound source audio signals using the multiple updated audio tracks and the constructed VAD data.SELECTED DRAWING: Figure 6

Aspects of the present invention provide systems and methods for distributed signal processing of indoor localization signals wherein statistical algorithms and machine learning are used in place of a fingerprint map. The disclosure relates to calculation of angle and distance based on measurements of an indoor localization signal, followed by energy-efficient distribution of signal processing. Local signal processing is performed using any of multiple eigen structure algorithms or a linear probabilistic inference, before cloud-based signal processing is performed using a nonlinear probabilistic inference and machine learning that's been trained with historical data transmitted by the base stations and time-of-day location patterns. Without having to generate and constantly update an energy-exorbitant fingerprint map, the disclosed system reduces localization error to merely 50 cm with 95% probability without compromising energy-efficiency to rival the accuracy of indoor localization systems that utilize fingerprinting.

Aspects of the present invention provide systems and methods for distributed signal processing of indoor localization signals wherein statistical algorithms and machine learning are used in place of a fingerprint map. The disclosure relates to calculation of angle and distance based on measurements of an indoor localization signal, followed by energy-efficient distribution of signal processing. Local signal processing is performed using any of multiple eigen structure algorithms or a linear probabilistic inference, before cloud-based signal processing is performed using a nonlinear probabilistic inference and machine learning that's been trained with historical data transmitted by the base stations and time-of-day location patterns. Without having to generate and constantly update an energy-exorbitant fingerprint map, the disclosed system reduces localization error to merely 50 cm with 95% probability without compromising energy-efficiency to rival the accuracy of indoor localization systems that utilize fingerprinting.

Aspects of embodiments pertain to systems for estimating direction of arrival and delays of reflected and non-reflected sources in a room received by a plurality of sound capturing devices in the room, comprising obtaining sound data descriptive of received sources. Embodiments may further comprise determining, based on the sound data, a correlation value between each two audio channels of a plurality of audio channels to obtain, for a same frequency of the plurality of frequencies, a set of correlation values; and to perform a transform on each set of frequency-related correlation values to obtain a plurality of subsets of delay representations of a 3D Matrix. Each set of time delay representations may be analyzed or processed to extract information about the sources.

Embodiments described herein provide a combined multi-source time difference of arrival (TDOA) tracking and voice activity detection (VAD) mechanism that can be applicable to general array geometries, e.g., microphone arrays located on a plane. The combined multi-source TDOA tracking and VAD mechanism scans azimuth and elevation angles of the microphone array in microphone pairs based on which a planar locus of physically allowable TDOAs can be formed in a multi-dimensional TDOA space of multiple microphone pairs. In this way, the multi-dimensional TDOA tracking reduces the number of computations typically involved in conventional TDOA by performing a TDOA search separately for each dimension.

Implementations can detect respective audio data that captures an acoustic event at multiple assistant devices in an ecosystem that includes a plurality of assistant devices, process the respective audio data locally at each of the multiple assistant devices to generate respective measures that are associated with the acoustic event using respective event detection models, process the respective measures to determine whether the detected acoustic event is an actual acoustic event, and cause an action associated with the actional acoustic event to be performed in response to determining that the detected acoustic event is the actual acoustic event. In some implementations, the multiple assistant devices that detected the respective audio data are anticipated to detect the respective audio data that captures the actual acoustic event based on a plurality of historical acoustic events being detected at each of the multiple assistant devices.

A system includes a phased antenna array configured to receive signals from at least one signal source not initially designed to provide a signal related to sound. The system also includes a signal processor configured to locate the at least one signal source using the received signals and detect a movement of the at least one signal source relative to the phased antenna array, wherein the detected movement is at least partially in response to sound waves impinging on the at least one signal source. The system further includes a sound demodulator configured to demodulate the sound waves from the detected movement.

The described aspects and implementations enable privacy-respecting detection, separation, and localization of sounds in vehicle environments. The techniques include obtaining, using audio detector(s) of a vehicle, a sound recording that includes a plurality of elemental sounds (ESs) in a driving environment of the vehicle, and processing, using a sound separation model, the sound recording to separate individual ESs of the plurality of ESs. The techniques further include identifying a content of individual ESs and causing a driving path of the vehicle to be modified in view of the identified content of the individual ESs. Further techniques include rendering speech imperceptibly by redacting temporal portions of the speech, using sound recognition models to identify and discard recordings of speech, and driving at speeds that exceed threshold speeds at which speech becomes imperceptible from noise masking.

A system includes a phased antenna array configured to receive signals from at least one signal source not initially designed to provide a signal related to sound. The system also includes a signal processor configured to locate the at least one signal source using the received signals and detect a movement of the at least one signal source relative to the phased antenna array, wherein the detected movement is at least partially in response to sound waves impinging on the at least one signal source. The system further includes a sound demodulator configured to demodulate the sound waves from the detected movement.

Embodiments described herein provide a combined multi-source time difference of arrival (TDOA) tracking and voice activity detection (VAD) mechanism that can be applicable to general array geometries, e.g., microphone arrays located on a plane. The combined multi-source TDOA tracking and VAD mechanism scans azimuth and elevation angles of the microphone array in microphone pairs based on which a planar locus of physically allowable TDOAs can be formed in a multi-dimensional TDOA space of multiple microphone pairs. In this way, the multi-dimensional TDOA tracking reduces the number of computations typically involved in conventional TDOA by performing a TDOA search separately for each dimension.

This application provides an in-vehicle user positioning method, an in-vehicle interaction method, a vehicle-mounted apparatus, and a vehicle. In an example, the in-vehicle user positioning method includes: obtaining a sound signal collected by an in-vehicle microphone; in response to that a first voice command is recognized from the sound signal, determining a first user who sends the first voice command; and determining an in-vehicle location of the first user based on a mapping relationship between an in-vehicle user and an in-vehicle location.

Examples are disclosed that relate to adaptively adjusting a radio frequency (RF) signal to suppress interference with radio operation of a prioritized RF system while maintaining radio operation of a non-prioritized RF system. In one example, a system includes an antenna array, a RF signal detection subsystem, and a signal-forming subsystem. The RF signal detection subsystem is configured to detect a prioritized RF signal in a prioritized RF spectrum band and determine an angle of arrival of the prioritized RF signal. The signal-forming subsystem is configured to output a non-prioritized RF signal based at least on the angle of arrival. A radiation null is formed along the angle of arrival in a radiation pattern of the non-prioritized RF signal. The signal-forming subsystem is configured to transmit the non-prioritized RF signal via the antenna array.

One embodiment provides a method of sound source enumeration and direction of arrival (DoA) estimation. The method, the method includes estimating, by an enumeration module, a number of sound sources associated with an acoustic signal. The estimating includes selecting a specific parametric model from a generalized model. The generalized model is related to a microphone array architecture used to capture the acoustic signal. The method further includes estimating, by a DoA module, a direction of arrival of each sound source of the number of sound sources based, at least in part, on the selected model. The estimating the number of sound sources and estimating the DoA of each sound source are performed using a Bayesian framework.

The described aspects and implementations enable privacy-respecting detection, separation, and localization of sounds in vehicle environments. The techniques include obtaining, using audio detector(s) of a vehicle, a sound recording that includes a plurality of elemental sounds (ESs) in a driving environment of the vehicle, and processing, using a sound separation model, the sound recording to separate individual ESs of the plurality of ESs. The techniques further include identifying a content of individual ESs and causing a driving path of the vehicle to be modified in view of the identified content of the individual ESs. Further techniques include rendering speech imperceptibly by redacting temporal portions of the speech, using sound recognition models to identify and discard recordings of speech, and driving at speeds that exceed threshold speeds at which speech becomes imperceptible from noise masking.

Provided is a device control method and apparatus. The method is applied to an audio device, and includes: receiving (101) an acoustic signal set, determining (102) a propagation characteristic of an acoustic signal in the acoustic signal set, determining (103), according to the propagation characteristic, a device parameter associated with audio play quality to be used by the audio device, and controlling (104) the audio device to play audio with the device parameter.