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.