  Recently, progressive learning has shown its capacity to improve speech
quality and speech intelligibility when it is combined with deep neural network
(DNN) and long short-term memory (LSTM) based monaural speech enhancement
algorithms, especially in low signal-to-noise ratio (SNR) conditions.
Nevertheless, due to a large number of parameters and high computational
complexity, it is hard to implement in current resource-limited
micro-controllers and thus, it is essential to significantly reduce both the
number of parameters and the computational load for practical applications. For
this purpose, we propose a novel progressive learning framework with causal
convolutional recurrent neural networks called PL-CRNN, which takes advantage
of both convolutional neural networks and recurrent neural networks to
drastically reduce the number of parameters and simultaneously improve speech
quality and speech intelligibility. Numerous experiments verify the
effectiveness of the proposed PL-CRNN model and indicate that it yields
consistent better performance than the PL-DNN and PL-LSTM algorithms and also
it gets results close even better than the CRNN in terms of objective
measurements. Compared with PL-DNN, PL-LSTM, and CRNN, the proposed PL-CRNN
algorithm can reduce the number of parameters up to 93%, 97%, and 92%,
respectively.
