Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks In recent decades, neural network based methods have significantly improved the performace of speech enhancement. Most of them estimate timefrequency (TF) representation of target speech directly or indirectly, then resynthesize waveform using the estimated TF representation. In this work, we proposed the temporal convolutional recurrent network (TCRN), an endtoend model that directly map noisy waveform to clean waveform. The TCRN, which is combined convolution and recurrent neural network, is able to efficiently and effectively leverage shortterm ang longterm information. Futuremore, we present the architecture that repeatedly downsample and upsample speech during forward propagation. We show that our model is able to improve the performance of model, compared with existing convolutional recurrent networks. Futuremore, We present several key techniques to stabilize the training process. The experimental results show that our model consistently outperforms existing speech enhancement approaches, in terms of speech intelligibility and quality.

An RNNbased Speech Enhancement Method for a Binaural Hearing Aid System This paper presents a recurrent neural network (RNN)based speech enhancement method for a binaural hearing aid system. We design a binaural RNN architecture to train gains of subbands. The proposed method trains the binaural recurrent neural network from binaural signals separately before concatenating to a weighting fully connected layer. The method preserves more cues from binaural signals using different training strategies and weighting variations. The results show the proposed method achieves a balance between speech intelligence and noise suppression in different acoustics scene. We demonstrate that the proposed method can implement speech enhancement for hearing aids with a low complexity neural network. This makes it applicable on smartphone or embedded devices.