Spectral domain texture analysis for speech enhancement

摘要

A fast algorithm which aims at performing texture analysis of time–frequency images for denoising purposes is described in this paper. Time–frequency images are built using the peaks of the amplitude spectrum computed on a noisy speech signal. Using texture analysis, we can look at the spectral 2D information on a large scale, thus allowing the correction of spectral continuity by restoring peaks corrupted by noise which can appear as missing or modified. The algorithm has been used in preprocessors of speech processing systems. In fact, we report interesting results obtained with this algorithm in speech enhancement and HMM speech recognition tasks, especially for noise types which are quite difficult to treat with conventional algorithms, such as micro-interruptions or bursts of tonal noise at random frequencies.