Comparative analysis of voice denoising using machine learning and traditional denoising
Ke Tang · Applied and Computational Engineering · 2024
Noise often affects the content of an audio signal, and noise reduction techniques can help retrieve the original speech content. In recent years, AI-based noise reduction has witnessed rapid development. This article provides a brief introduction to the background and principles of several AI-based noise reduction methods. One of the mentioned methods is an end-to-end time-domain deep learning speech division algorithm, which utilizes a multi-layer CNN network framework. Due to the need for deep network architectures to extract features, it involves a higher computational load. Traditional noise reduction algorithms, on the other hand, are based on researchers' understanding of noise patterns and modeling. Traditional methods may not perform well on non-stationary noise, but they are relatively simple in terms of algorithmic implementation. Through a comparison from various perspectives, AI-based noise reduction demonstrates superior performance in known environments compared to traditional methods. However, in unknown environments, AI-based noise reduction may encounter performance anomalies. Combining AI-based and traditional noise reduction techniques can provide better stability and higher performance in certain scenarios.