  In recent years, Long Short-Term Memory (LSTM) has become a popular choice
for speech separation and speech enhancement task. The capability of LSTM
network can be enhanced by widening and adding more layers. However, this would
introduce millions of parameters in the network and also increase the
requirement of computational resources. These limitations hinders the efficient
implementation of RNN models in low-end devices such as mobile phones and
embedded systems with limited memory. To overcome these issues, we proposed to
use an efficient alternative approach of reducing parameters by representing
the weight matrix parameters of LSTM based on Tensor-Train (TT) format. We
called this Tensor-Train factorized LSTM as TT-LSTM model. Based on this
TT-LSTM units, we proposed a deep TensorNet model for single-channel speech
enhancement task. Experimental results in various test conditions and in terms
of standard speech quality and intelligibility metrics, demonstrated that the
proposed deep TT-LSTM based speech enhancement framework can achieve
competitive performances with the state-of-the-art uncompressed RNN model, even
though the proposed model architecture is orders of magnitude less complex.

Sound;Machine Learning;Audio and Speech Processing