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.

Researchers

Christopher Smalt / Gregory Ciccarelli / Joseph Perricone / Thomas Quatieri / Michael Brandstein / Paul Calamia / Stephanie Haro / Michael Nolan / Nima Mesgarani / James O'Sullivan

Departments: Lincoln Laboratory
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Bioinformatics / Sensing & Imaging: Acoustics

  • end to end deep neural network for auditory attention decoding
    United States of America | Granted | 11,630,513

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