A sound collecting apparatus includes a base of a substantially spherical body and a plurality of microphones provided on the base, a number of the microphones having a predetermined constraint, the plurality of microphones being alternately arranged vertically relative to a horizontal plane including a center of the substantially spherical body in order to improve resolution in a horizontal direction.

Speech received from a microphone array is enhanced. In one example, a noise filtering system receives audio from the plurality of microphones, determines a beamformer output from the received audio, applies a first auto-regressive moving average smoothing filter to the beamformer output, determines noise estimates from the received audio, applies a second auto-regressive moving average smoothing filter to the noise estimates, and combines the first and second smoothing filter outputs to produce a power spectral density output of the received audio with reduced noise.

Noise is suppressed from a microphone array by estimating a noise field isotropy. In some examples audio is received from a plurality of microphones. A power spectral density of a beamformer output is determined and a power spectral density of microphone noise differences is determined. A noise power spectral density is determined using a transfer function and the noise power spectral density is applied to the beamformer output power spectral density to produce a power spectral density output of the received audio with reduced noise.

An audio system for a headset includes a plurality of speakers and an audio controller. The plurality of speakers may be in a dipole configuration that cancel sound leakage into a local area of the headset. The controller filters audio content presented by the plurality of speakers to further mitigate leakage of audio content into the local area. The audio determines sound filters based on environmental conditions, such as ambient noise levels, as well as based on the audio content being presented.

An audio data processing method is provided. The method includes: obtaining multi-path audio data in an environmental space, obtaining a speech data set based on the multi-path audio data, and separately generating, in a plurality of enhancement directions, enhanced speech information corresponding to the speech data set; matching a speech hidden feature in the enhanced speech information with a target matching word, and determining an enhancement direction corresponding to the enhanced speech information having a highest degree of matching with the target matching word as a target audio direction; obtaining speech spectrum features in the enhanced speech information, and obtaining, from the speech spectrum features, a speech spectrum feature in the target audio direction; and performing speech authentication on the speech hidden feature and the speech spectrum feature that are in the target audio direction based on the target matching word, to obtain a target authentication result.

An example method of operation may include identifying, in a particular room environment, a number of speakers and one or more microphones on a network controlled by a controller and amplifier, providing test signals to play sequentially from each amplifier channel of the amplifier and the speakers, monitoring the test signals from the one or more microphones simultaneously to detect operational speakers and amplifier channels, providing additional test signals to the speakers to determine tuning parameters, detecting the additional test signals at the one or more microphones controlled by the controller, and automatically establishing a background noise level and noise spectrum of the room environment based on the detected additional test signals.

Techniques are provided for audio-based detection and tracking of an acoustic source. A methodology implementing the techniques according to an embodiment includes generating acoustic signal spectra from signals provided by a microphone array, and performing beamforming on the acoustic signal spectra to generate beam signal spectra, using time-frequency masks to reduce noise. The method also includes detecting, by a deep neural network (DNN) classifier, an acoustic event, associated with the acoustic source, in the beam signal spectra. The DNN is trained on acoustic features associated with the acoustic event. The method further includes performing pattern extraction, in response to the detection, to identify time-frequency bins of the acoustic signal spectra that are associated with the acoustic event, and estimating a motion direction of the source relative to the array of microphones based on Doppler frequency shift of the acoustic event calculated from the time-frequency bins of the extracted pattern.

An over-the-ear hearing assessment device and a method for evaluating the performance of over-the-ear hearing devices are described. An exemplary over-the-ear hearing assessment device includes an ear cup defining an interior volume and positionable at least partially over the ear of a user. The ear cup includes a shell, a cushion, and an acoustic port extending from an exterior to the interior volume of the ear cup. The acoustic port is sealably engagable with a microphone.

An apparatus comprising a first part, the first part having: at least one camera configured to capture images, and at least two microphones configured to capture at least two audio signals; and a second part having at least one microphone configured to capture at least one audio signal, wherein one of the first part or the second part is configured to perform a move relative to the other part, wherein the apparatus is configured to: determine a parameter associated with the move; select at least one of: the at least two audio signals, or the at least one audio signal based, at least partially, on the determined parameter; and generate at least one output audio signal based on the parameter associated with the move and the selected at least one of: the at least two audio signals or, the at least one audio signal.

Provided is a target sound extraction technique based on a steering vector generation method enabling instability in a calculation to be prevented when a neural network is trained by using an error back propagation method to reduce an estimation error of a beamformer. A target sound signal generation apparatus generates a target sound signal y.sub.t,f corresponding to a target sound included in an observed sound from an observed signal vector x.sub.t,f corresponding to the observed sound collected by using a plurality of microphones. The target sound signal generation apparatus includes a mask generation unit, a steering vector generation unit, a beamformer vector generation unit, and a target sound signal generation unit. The mask generation unit is configured as a neural network trained by using an error back propagation method. The steering vector generation unit generates a steering vector h.sub.f by determining an eigenvector corresponding to a maximum eigenvalue of a predetermined matrix generated from the observed signal vector x.sub.t,f and a mask γ.sub.t,f by using a power method.