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In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement. However, due to the limitation of training data and computational complexity, real-time enhancement of full-band (48 kHz) speech signals is still very challenging. Because of the low energy of spectral information in the high-frequency part, it is more difficult to directly model and enhance the full-band spectrum using neural networks. To solve this problem, this paper proposes a two-stage real-time speech enhancement model with extraction-interpolation mechanism for a full-band signal. The 48 kHz full-band time-domain signal is divided into three sub-channels by extracting, and a two-stage processing scheme of `masking + compensation' is proposed to enhance the signal in the complex domain. After the two-stage enhancement, the enhanced full-band speech signal is restored by interval interpolation. In the subjective listening and word accuracy test, our proposed model achieves superior performance and outperforms the baseline model overall by 0.59 MOS and 4.0% WAcc for the non-personalized speech denoising task., Comment: Accepted by ICASSP 2022, Deep Noise Suppression Challenge

Recent single-channel speech enhancement methods based on deep neural networks (DNNs) have achieved remarkable results, but there are still generalization problems in real scenes. Like other data-driven methods, DNN-based speech enhancement models produce significant performance degradation on untrained data. In this study, we make full use of the contribution of multi-target joint learning to the model generalization capability, and propose a lightweight and low-computing dilated convolutional network (DCN) model for a more robust speech denoising task. Our goal is to integrate the masking target, the mapping target, and the parameters of the traditional speech enhancement estimator into a DCN model to maximize their complementary advantages. To do this, we build a multi-stage learning framework to deal with multiple targets in stages to achieve their joint learning, namely `MT-in-MS'. Our experimental results show that compared with the state-of-the-art time domain and time-frequency domain models, this proposed low-cost DCN model can achieve better generalization performance in speaker, noise, and channel mismatch cases., Comment: Submitted to APSIPA-ASC 2021

The most recent deep neural network (DNN) models exhibit impressive denoising performance in the time-frequency (T-F) magnitude domain. However, the phase is also a critical component of the speech signal that is easily overlooked. In this paper, we propose a multi-branch dilated convolutional network (DCN) to simultaneously enhance the magnitude and phase of noisy speech. A causal and robust monaural speech enhancement system is achieved based on the multi-objective learning framework of the complex spectrum and the ideal ratio mask (IRM) targets. In the process of joint learning, the intermediate estimation of IRM targets is used as a way of generating feature attention factors to realize the information interaction between the two targets. Moreover, the proposed multi-scale dilated convolution enables the DCN model to have a more efficient temporal modeling capability. Experimental results show that compared with other state-of-the-art models, this model achieves better speech quality and intelligibility with less computation.

Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by the ability to capture the long-term effective history information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but come with the issues of the high latency and the complexity of training.To address these issues, the temporal convolutional network (TCN) was proposed to replace the RNNs in various sequence modeling tasks. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement.The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity.Inspired by the TCN, the MB-TCN model incorporates one dimensional causal dilated CNN and residual learning to expand receptive fields for capturing long-term temporal contextual information.Our extensive experimental investigation suggests that the MB-TCNs outperform the residual long short-term memory networks (ResLSTMs), temporal convolutional networks (TCNs), and the CNN networks that employ dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-TCN model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics., Comment: There are some inappropriate decriptions. These descriptions exist on many pages