Intel Corporation

Marek Zabkiewicz

Kuba Lopatka

Tobias Bocklet

Przemyslaw Maziewski

Adam Kupryjanow

Lukasz Kurylo

Karol Duzinkiewicz

Mateusz Kotarski

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.

Audio-based detection and tracking of emergency vehicles

Techniques are provided for efficient acoustic event detection with reduced resource consumption. A methodology implementing the techniques according to an embodiment includes calculating frames of power spectra based on segments of received acoustic signals. The method further includes two processes, one for detecting impulsive acoustic events and another for detecting continuous acoustic events. The first process includes generating impulsive acoustic event features associated with first and second power spectrum frames, applying a neural network classifier to the impulsive acoustic event features to generate event scores, and detecting an impulsive acoustic event based on those event scores. The second process includes generating reduced-dimension continuous acoustic event features associated with the first and second power spectrum frames, applying a neural network classifier to the reduced-dimension continuous acoustic event features to generate a second set of event scores, and detecting a continuous acoustic event based on the second set of event scores.

Acoustic event detector with reduced resource consumption

Growing community of inventors

Gdynia, Poland

Marek Zabkiewicz