Audio-based detection and tracking of emergency vehicles

At least one non-transitory computer readable storage medium comprising instructions encoded thereon that, when executed, cause one or more processors to at least:

• perform beamforming on a plurality of acoustic signal spectra to generate at least a first beam signal spectrum in a first direction and a second beam signal spectrum in a second direction different from the first direction, the acoustic signal spectra generated from acoustic signals received from an array of microphones;
• detect, by a deep neural network (DNN) classifier, an acoustic event associated with an acoustic source, in at least one of the first beam signal spectrum or the second beam signal spectrum;
• estimate, by the deep neural network (DNN) classifier, a direction of the acoustic source of at least one of the first direction or the second direction;
• perform:
• first pattern extraction in response to the detection of the acoustic event, the first pattern extraction associated with a first power state of the one or more processors;
• and
• second pattern extraction prior to the detection of the acoustic event, the second pattern extraction associated with a second power state of the one or more processors, the second power state consuming less power than the first power state, the pattern comprising identified time and frequency bins of the plurality of acoustic signal spectra, the bins associated with the acoustic event;
• and
• estimate a direction of motion of the acoustic source relative to the array of microphones, the estimation based on a Doppler frequency shift of the acoustic event, the Doppler frequency shift calculated from the time and frequency bins of the extracted pattern.

A system for audio-based detection and tracking of an acoustic source, the system comprising:

• a beamforming circuit to perform beamforming on a plurality of acoustic signal spectra to generate at least a first beam signal spectrum in a first direction and a second beam signal spectrum in a second direction different from the first direction, the acoustic signal spectra generated from acoustic signals received from an array of microphones;
• a deep neural network (DNN) classifier to:
• detect an acoustic event associated with the acoustic source, in at least one of the first beam signal spectrum or the second beam signal spectrum;
• and
• estimate a direction of the acoustic source of at least one of the first direction or the second direction;
• a pattern extraction circuit to perform:
• first pattern extraction instructions in response to the detection of the acoustic event, the first pattern extraction instructions associated with a first power state of the pattern extraction circuit;
• and
• second pattern extraction instructions prior to the detection of the acoustic event, the second pattern extraction instructions associated with a second power state of the pattern extraction circuit, the second power state consuming less power than the first power state, the pattern comprising identified time and frequency bins of the plurality of acoustic signal spectra, the bins associated with the acoustic event;
• and
• a movement direction estimation circuit to estimate a direction of motion of the acoustic source relative to the array of microphones, the estimation based on a Doppler frequency shift of the acoustic event, the Doppler frequency shift calculated from the time and frequency bins of the extracted pattern.