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ON LOSS FUNCTIONS FOR DEEP-LEARNING BASED T60 ESTIMATION

Citation Author(s):
Yuying Li, Yuchen Liu, Donald Williamson
Submitted by:
Yuying Li
Last updated:
22 June 2021 - 10:59am
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Yuying Li

Abstract 

Abstract: 

Reverberation time, T60, directly influences the amount of reverberation
in a signal, and its direct estimation may help with
dereverberation. Traditionally, T60 estimation has been done
using signal processing or probabilistic approaches, until recently
where deep-learning approaches have been developed.
Unfortunately, the appropriate loss function for training the
network has not been adequately determined. In this paper,
we propose a composite classification- and regression-based
cost function for training a deep neural network that predicts
T60 for a variety of reverberant signals. We investigate pure classification,
pure-regression, and combined classification-regression
based loss functions, where we additionally incorporate
computational measures of success. Our results reveal
that our composite loss function leads to the best performance
as compared to other loss functions and comparison
approaches. We also show that this combined loss function
helps with generalization.

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Dataset Files

ICASSP2021 Poster_Grace.V2.pdf

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