  This paper aims to address two issues existing in the current speech
enhancement methods: 1) the difficulty of phase estimations; 2) a single
objective function cannot consider multiple metrics simultaneously. To solve
the first problem, we propose a novel convolutional neural network (CNN) model
for complex spectrogram enhancement, namely estimating clean real and imaginary
(RI) spectrograms from noisy ones. The reconstructed RI spectrograms are
directly used to synthesize enhanced speech waveforms. In addition, since
log-power spectrogram (LPS) can be represented as a function of RI
spectrograms, its reconstruction is also considered as another target. Thus a
unified objective function, which combines these two targets (reconstruction of
RI spectrograms and LPS), is equivalent to simultaneously optimizing two
commonly used objective metrics: segmental signal-to-noise ratio (SSNR) and
logspectral distortion (LSD). Therefore, the learning process is called
multi-metrics learning (MML). Experimental results confirm the effectiveness of
the proposed CNN with RI spectrograms and MML in terms of improved standardized
evaluation metrics on a speech enhancement task.
