  We propose a training method for deep neural network (DNN)-based source
enhancement to increase objective sound quality assessment (OSQA) scores such
as the perceptual evaluation of speech quality (PESQ). In many conventional
studies, DNNs have been used as a mapping function to estimate time-frequency
masks and trained to minimize an analytically tractable objective function such
as the mean squared error (MSE). Since OSQA scores have been used widely for
sound-quality evaluation, constructing DNNs to increase OSQA scores would be
better than using the minimum-MSE to create high-quality output signals.
However, since most OSQA scores are not analytically tractable, \textit{i.e.},
they are black boxes, the gradient of the objective function cannot be
calculated by simply applying back-propagation. To calculate the gradient of
the OSQA-based objective function, we formulated a DNN optimization scheme on
the basis of \textit{black-box optimization}, which is used for training a
computer that plays a game. For a black-box-optimization scheme, we adopt the
policy gradient method for calculating the gradient on the basis of a sampling
algorithm. To simulate output signals using the sampling algorithm, DNNs are
used to estimate the probability density function of the output signals that
maximize OSQA scores. The OSQA scores are calculated from the simulated output
signals, and the DNNs are trained to increase the probability of generating the
simulated output signals that achieve high OSQA scores. Through several
experiments, we found that OSQA scores significantly increased by applying the
proposed method, even though the MSE was not minimized.
