Introduction
Universal speech enhancement aims to improve speech signals recorded under various adverse conditions and distortions, including noise, reverberation, clipping, equalization (EQ) distortion, packet loss, codec loss, bandwidth limitations, and other forms of degradation. A comprehensive universal speech enhancement system integrates multiple techniques such as noise suppression, dereverberation, equalization, packet loss concealment, bandwidth extension, declipping, and other enhancement methods to produce speech signals that closely approximate studio-quality audio.
In this demonstration, I have employed several advanced speech enhancement techniques, including GAN-based and score diffusion-based methods, to enhance speech signals degraded by various types of distortions. The primary objective of this demo is to illustrate the effectiveness of these diverse speech enhancement techniques across different types of audio distortions. Most of the samples are drawn from microsoft SIG-Challenge blind data set.
Tasks and Samples
1. Hybrid Distortion Compensation
2. Sound Quality Enhancement
3. Equalization Compensation
4. Noise Suppression
5. Dereverberation
6. Declipping
7. Packet Loss Compensation
8. Bandwidth Extension
Comparisons
Comparison of the enhanced results of different open-source speech restoration and enhancement methods: Voice Fixer and Resemble Enhance. As illustrated, the enhanced results of SGMSE + GAN are much better than that of Voice Fixer, and Resemble Enhance is close to SGMSE + GAN.
