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  3. Cog Lunch: Andrew Fancl "Deep neural network models of sound localization reveal how perception is adapted to real-world environments"
Cog Lunch: Andrew Fancl "Deep neural network models of sound localization reveal how perception is adapted to real-world environments"
Department of Brain and Cognitive Sciences (BCS)

Cog Lunch: Andrew Fancl "Deep neural network models of sound localization reveal how perception is adapted to real-world environments"

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Add to CalendarAmerica/New_YorkCog Lunch: Andrew Fancl "Deep neural network models of sound localization reveal how perception is adapted to real-world environments"02/15/2022 12:00 pm02/15/2022 1:00 pm,
February 15, 2022
12:00 pm - 1:00 pm
Location
,
Contact
hopekean@mit.edu
    Description

    Speaker: Andrew Francl

    Lab: McDermott

     

    Title: Deep neural network models of sound localization reveal how perception is adapted to real-world environments

     

    Abstract: Mammals localize sounds using information from their two ears. Localization in real-world conditions is challenging, as echoes provide erroneous information, and noises mask parts of target sounds. To better understand real-world localization we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. The resulting model localized accurately in realistic conditions with noise and reverberation. In simulated experiments, the model exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, biases for sound onsets, and limits on localization of concurrent sources. But when trained in unnatural environments without either reverberation, noise, or natural sounds, these performance characteristics deviated from those of humans. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can reveal the real-world constraints that shape perception.

     

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