Modern deep learning-based models have seen outstanding performance improvement with
speech enhancement tasks. The number of parameters of state-of-the-art models, however …

Speech enhancement approaches based on deep neural network have outperformed the
traditional signal processing methods. This paper presents a low-delay speech …

Speech exhibits strong dependencies among its samples in both time and frequency
domains. In this paper, we propose a low-complexity composite model for speech …

摘要全卷积时域音频分离网络(Conv-TasNet) 是近年提出的一种主流的端对端语音分离模型.
Conv-TasNet 利用膨胀卷积扩大感受野, 使其在空间上可以融合更多语音特征 …

Convolutional neural networks (CNN) have made remarkable achievements in speech
enhancement. However, the convolution operation is difficult to obtain the global context of …

The speech denoising model based on adversarial generative network has achieved better
results than the traditional machine learning model. In this paper, for the short cut connection …