  Recently, deep neural network (DNN) has made a breakthrough in monaural
source enhancement. Through a training step by using a large amount of data,
DNN estimates a mapping between mixed signals and clean signals. At this time,
we use an objective function that numerically expresses the quality of a
mapping by DNN. In the conventional methods, L1 norm, L2 norm, and
Itakura-Saito divergence are often used as objective functions. Recently, an
objective function based on short-time objective intelligibility (STOI) has
also been proposed. However, these functions only indicate similarity between
the clean signal and the estimated signal by DNN. In other words, they do not
show the quality of noise reduction or source enhancement. Motivated by the
fact, this paper adopts signal-to-distortion ratio (SDR) as the objective
function. Since SDR virtually shows signal-to-noise ratio (SNR), maximizing SDR
solves the above problem. The experimental results revealed that the proposed
method achieved better performance than the conventional methods.
