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Application US20200201435
Published 2020-06-25
End-to-end Deep Neural Network For Auditory Attention Decoding
In one aspect of the present disclosure, method includes: receiving neural data responsive to a listener's auditory attention; receiving an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources: generating, from the received acoustic signal, audio data comprising one or more features of the acoustic source, forming combined data representative of the neural data and the audio data, and providing the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and using the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.
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- 1. A method comprising:
receiving neural data responsive to a listener's auditory attention; receiving an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources:
generating, from the received acoustic signal, audio data comprising one or more features of the acoustic source,
forming combined data representative of the neural data and the audio data, and
providing the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and
using the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.
- 10. An apparatus comprising:
a neural sensor interface configured to receive one or more neural signals responsive to a listener's auditory attention; an audio input configured to receive an acoustic signal responsive to a plurality of acoustic sources; and a processor configured to:
process the one or more neural signals to generate multi-channel neural data;
for each of the plurality of acoustic sources:
generate, from the received acoustic signal, audio data comprising one or more features of the acoustic source,
form combined data representative of the neural data and the audio data, and
provide the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and
identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention based on the calculated similarity scores.
- 19. A non-transitory computer-readable medium storing program instructions that are executable to:
receive neural data responsive to a listener's auditory attention; receive an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources:
generate, from the received acoustic signal, audio data comprising one or more features of the acoustic source,
form combined data representative of the neural data and the audio data, and
provide the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and
use the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.
