  We propose a speech enhancement method using a causal deep neural
network~(DNN) for real-time applications. DNN has been widely used for
estimating a time-frequency~(T-F) mask which enhances a speech signal. One
popular DNN structure for that is a recurrent neural network~(RNN) owing to its
capability of effectively modelling time-sequential data like speech. In
particular, the long short-term memory (LSTM) is often used to alleviate the
vanishing/exploding gradient problem which makes the training of an RNN
difficult. However, the number of parameters of LSTM is increased as the price
of mitigating the difficulty of training, which requires more computational
resources. For real-time speech enhancement, it is preferable to use a smaller
network without losing the performance. In this paper, we propose to use the
equilibriated recurrent neural network~(ERNN) for avoiding the
vanishing/exploding gradient problem without increasing the number of
parameters. The proposed structure is causal, which requires only the
information from the past, in order to apply it in real-time. Compared to the
uni- and bi-directional LSTM networks, the proposed method achieved the similar
performance with much fewer parameters.
