Single-Channel Speech Enhancement Method Based on Deep Neural Networks

Abstract

In practical voice communication and processing, speech signals are extremely sensitive to background noise, equipment noise, and interference from complex environments, leading to a decline in speech quality and clarity. This, in turn, affects the overall performance of downstream systems such as speech recognition, speech coding, and human-computer interaction. Therefore, researching efficient and robust single-channel speech signal enhancement methods has significant theoretical and practical implications. With the rapid development of deep learning technology, deep neural networks, with their superior nonlinear feature modeling capabilities and end-to-end training advantages, have become a core research area for single-channel speech enhancement. Given the limitations of traditional speech enhancement methods in complex, non-stationary, and noisy environments, this paper proposes a deep neural network-based speech enhancement method to address the challenges of single-channel speech enhancement. This method aims to extract a clearer, more natural, and more intelligible target signal from noisy speech. This paper first analyzes the basic principles of speech quality enhancement, including the characteristics of speech signals, types of background noise, time-domain and frequency-domain speech representation methods, and commonly used speech quality evaluation metrics. Based on this, this paper develops a deep neural network model for single-channel speech enhancement. This model effectively estimates the spectral information of the target speech by learning the spectral features of noisy speech.