A two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing, the method includes: deploying a coprime planar array; modeling a tensor of the received signals; deriving the second-order equivalent signals of an augmented virtual array based on cross-correlation tensor transformation; deploying a three-dimensional coarray tensor of the virtual array; deploying a five-dimensional coarray tensor based on a coarray tensor dimension extension strategy; forming a structured coarray tensor including three-dimensional spatial information; and achieving two-dimensional direction-of-arrival estimation through CANDECOMP/PARACFAC decomposition. The present disclosure constructs a processing framework of a structured coarray tensor based on statistical analysis of coprime planar array tensor signals, to achieve multi-source two-dimensional direction-of-arrival estimation in the underdetermined case on the basis of ensuring the performance such as resolution and estimation accuracy, and can be used for multi-target positioning.

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.

An apparatus determines a spatial position of an audio source in multi moving audio sources scenarios. The apparatus receives audio signal versions as local sound waves. The apparatus determines first and second probabilities for a direction of arrival of the audio signal version based on the audio signal versions received within a first time interval; determines third and fourth probabilities for the direction of arrival of the audio signal version based on the audio signal versions received within a second time interval; determines a first probability difference between the first and third probabilities; determines a second probability difference between the second and fourth probabilities; combines the third probability and the first probability difference to obtain an updated third probability; combines the fourth probability with the second probability difference to obtain an updated fourth probability; and determines the spatial position based on the updated third and fourth probabilities.

A wireless communication system includes a control station and a plurality of second wireless communication apparatuses. The control station includes a management table that holds sending permission information indicating whether or not to transmit a packet for requesting to send an orientation estimation auxiliary signal. The plurality of second wireless communication apparatuses each refer to the management table, transmit the packet to a first wireless communication apparatus in response to the sending permission information, and perform orientation estimation using the orientation estimation auxiliary signal transmitted from the first wireless communication apparatus.

A method and an apparatus for recognizing a voice are provided. The method may include: inputting a target voice into a pre-trained voice recognition model to obtain an initial text output by at least one recognition network in the voice recognition model, the recognition network including a plurality of preset types of processing layers, and at least one type of processing layer of the recognition network being obtained by training based on a voice sample in a preset direction interval; and determining a voice recognition result of the target voice, based on the initial text.

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.

Techniques for determining a direction of arrival of an emergency are discussed. A plurality of audio sensors of a vehicle can receive audio data associated with the vehicle. An audio sensor pair can be selected from the plurality of audio sensors to generate audio data representing sound in an environment of the vehicle. An angular spectrum associated with the audio sensor pair can be determined based on the audio data. A feature associated with the audio data can 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 feature using a machine learned model. An emergency sound (e.g., a siren) can be detected in the audio data and a direction associated with the emergency relative to the vehicle can be determined based on the feature and the DoA value.

A practically implementable robust direction-of-arrival (DoA) estimation approach that is resistant to localization errors due to mobility, multipath reflections, impulsive noise, and multiple-access interference. As part of the disclosed invention the inventors consider infrastructure-less 3D localization of autonomous underwater vehicles (AUVs) with no GPS assistance and no availability of global clock synchronization. The proposed method can be extended to challenging communication environments and applied for the localization of assets/objects in space, underground, intrabody, underwater and other complex, challenging, congested and sometimes contested environments. Each AUV leverages known-location beacon signals to self-localize and can simultaneously report its sensor data and measurement location. The approach uses two known location beacon nodes, where the beacons are single-hydrophone acoustic nodes that are deployed at known locations and transmit time-domain coded signals in a spread-spectrum fashion.

A sound source separation system includes: a controller that: acquires pieces of sound collection data with microphones that collect sounds output from first and second sound sources. The first sound source is at a first position at which effective distances from the microphones are equal and the second sound source is at a different position. The controller further acquires, based on the sound collection data, frequency spectra in two dimensions of a circumferential direction of a circle and a time direction. The first position is a center of the circle and each of the effective distances is a radius of the circle. The controller further separates, from the frequency spectra, a first sound source spectrum and a second sound source spectrum.

Provided is a direction of arrival estimation device wherein: a calculation circuit calculates a frequency weighting factor for each of a plurality of frequency components of signals recorded in a microphone array, on the basis of the differences among unit vectors indicating the directions of the sound sources of each of the plurality of frequency components; and an estimation circuit estimates the direction of arrival of a signal from the sound source, on the basis of the frequency weighting factors.