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.

An acoustic-based Direction of Arrival (DoA) system uses acoustic information to determine the direction of incoming sound, such as a person talking. The direction of the sound is then used to focus a laser-based time of flight (ToF) system to narrow the area of laser illumination, improving the signal to noise ratio because laser illumination is focused on the direction of the sound. The DoA system also provides elevation information pertaining to the source of the sound, to further narrow the required field of view of the laser ToF system.

A device for estimating Direction of Arrival (DOA) of sound from Q≥1 sound sources is provided. The device is configured to obtain a phase difference matrix, which includes measured phase difference values, each of the measured phase difference values being a measured value of a phase difference between two microphone units for a frequency bin in a range of frequencies of the sound. The device is further configured to generate a replicated phase difference matrix by replicating the measured phase difference values to other potential sinusoidal periods, calculate a DOA value for each phase difference value in the replicated phase difference matrix, and determine, as Q DOA results, the Q most prominent peak values in a histogram generated based on the calculated DOA values.

An ambient sound environment is captured by a microphone array of an autonomous vehicle traveling in the ambient sound environment. A perception module of the autonomous vehicle classifies sounds and localizes sound sources in the ambient sound environment. Classification is performed using spectrum analysis and/or machine learning. In an embodiment, sound sources within a field of view (FOV) of an image sensor of the autonomous vehicle are localized in a visual scene generated by the perception module. In an embodiment, one or more sound sources outside the FOV of the image sensors are localized in a static digital map. Localization is performed using parametric or non-parametric techniques and/or machine learning. The output of the perception module is input into a planning module of the autonomous vehicle to plan a route or trajectory for the autonomous vehicle in the ambient sound environment.

A motor vehicle includes an acoustic device configured to generate and capture acoustic waves, the acoustic device includes a vehicle part having a vibration region, and an actuator arranged thereon and configured to for excitation and detection of vibrations of the vehicle part in the vibration region, wherein the region is modified compared to an adjacent region of the vehicle part and has greater sensitivity to excitations in the frequency range of the acoustic wave.

A system configured to perform sound source localization (SSL) using reflection classification is provided. A device processes audio data representing sounds from multiple sound sources to generate sound track data that includes an individual sound track for each of the sound sources. To detect reflections, the device determines whether a pair of sound tracks are strongly correlated. For example, the device may calculate a correlation value for each pairwise combination of the sound tracks and determine whether the correlation value exceeds a threshold value. When the correlation value exceeds the threshold, the device invokes a reflection classifier trained to distinguish between direct sound sources and reflected sound sources. For example, the device extracts feature data from the pair of sound tracks and processes the feature data using a trained model to determine which of the sound tracks corresponds to the direct sound source.

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.

An estimator of direction of arrival (DOA) of speech from a far-field talker to a device in the presence of room reverberation and directional noise includes audio inputs received from multiple microphones and one or more beamformer outputs generated by processing the microphone inputs. A first DOA estimate is obtained by performing generalized cross-correlation between two or more of the microphone inputs. A second DOA estimate is obtained by performing generalized cross-correlation between one of the one or more beamformer outputs and one or more of: the microphone inputs and other of the one or more beamformer outputs. A selector selects the first or second DOA estimate based on an SNR estimate at the microphone inputs and a noise reduction amount estimate at the beamformer outputs. The SNR and noise reduction estimates may be obtained based on the detection of a keyword spoken by a desired talker.

A method and associated system for estimating a quantity representative of the sound energy at at least one point of a three-dimensional space where a plurality of antennas are situated, each including at least K acoustic sensors, K being higher than or equal to 2, includes for each antenna of the plurality of antennas, production of a plurality of signals representative of the sound field at the antenna in question, for each antenna of the plurality of antennas, determination of a raw value of the quantity at the point based on at least K+1 elements of a matrix that are based respectively on pairwise combinations of representative signals produced by the antenna in question, and determination of an estimated value of the quantity at the point by combining the raw values of the quantity at the point determined respectively for the various antennas of the plurality of antennas.

An aspect of the invention is directed to a system of both atmospheric and underwater sensors for measuring pressure waves from a noise source. A system of pressure sensors can be formed to determine the location of an external noise source, whether in air or underwater. The system includes at least two arrays consisting of pressure sensors, including at least one atmospheric pressure sensor and at least one underwater pressure sensor, such as a hydrophone. Each sensor may be a seven-fiber intensity modulated fiber optic pressure sensor. The system includes an analog to digital converter for digitizing the pressure data received from each sensor and a processor which processes the received signals to calculate an approximate location of the noise source based upon the pressure signals received by the sensors at different times of arrival. The system can provide this capability in remote applications due to its low power requirements.