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Speeding up machine hearing

delete2021-02-24
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PRE
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Laurel H. Carney *
DOI:10.1038/s42256-021-00317-ydelete
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Abstract

Abstract

En 中文
Computational models that capture the nonlinear processing of the inner ear have been prohibitively slow to use for most machine-hearing systems. A convolutional neural network model replicates hallmark features of cochlear signal processing, potentially enabling real-time applications.
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Nature Machine Intelligence cover
Nature Machine Intelligence
IF:
23.9
Papers:
1.3K
Citations:
1.5W

Organization

U
University of Rochester
Scholars:
2.6W
Papers: 2.1W
Citations: 2.2W
Cited Papers

Cited Papers

Hearing Is Believing
err2012-11-01
err45
PREAI
errStern, Richard M.; Morgan, Nelson
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err2013-05-03
err0
PREAI
err
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Random projections for Bayesian regression
err2015-11-19
err0
errOAAI
errLeo N. Geppert; Katja Ickstadt; Alexander Munteanu; Jens Quedenfeld; Christian Sohler
errShare
errSave
Deep Neural Network Model of Hearing-Impaired Speech-in-Noise Perception
err2020-12-15
err15
errOAAI
errHaro, Stephanie; Smalt, Christopher J.; Ciccarelli, Gregory A.; Quatieri, Thomas F.
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