  Most of the deep learning based speech enhancement (SE) methods rely on
estimating the magnitude spectrum of the clean speech signal from the observed
noisy speech signal, either by magnitude spectral masking or regression. These
methods reuse the noisy phase while synthesizing the time-domain waveform from
the estimated magnitude spectrum. However, there have been recent works
highlighting the importance of phase in SE. There was an attempt to estimate
the complex ratio mask taking phase into account using complex-valued
feed-forward neural network (FFNN). But FFNNs cannot capture the sequential
information essential for phase estimation. In this work, we propose a
realisation of complex-valued long short-term memory (RCLSTM) network to
estimate the complex ratio mask (CRM) using sequential information along time.
The proposed RCLSTM is designed to process the complex-valued sequences using
complex arithmetic, and hence it preserves the dependencies between the real
and imaginary parts of CRM and thereby the phase. The proposed method is
evaluated on the noisy speech mixtures formed from the Voice-Bank corpus and
DEMAND database. When compared to real value based masking methods, the
proposed RCLSTM improves over them in several objective measures including
perceptual evaluation of speech quality (PESQ), in which it improves by over
4.3%
