Characteristics of Wavelet Transform Multiscale Analysis for Enhanced Speech Denoising

By utilizing the multiscale analysis capabilities of wavelet transform, we enhance the basic frequency-domain spectral subtraction method through scale-specific threshold selection in the wavelet domain. The implementation involves applying different thresholding strategies (such as hard or soft thresholding) at each decomposition level using functions like wavedec and waverec for wavelet decomposition/reconstruction. The algorithm separately processes voiced and unvoiced segments by detecting zero-crossing rates and energy levels, preserving critical unvoiced components through conditional processing. Furthermore, adaptive algorithms like LMS (Least Mean Squares) or RLS (Recursive Least Squares) can be integrated to dynamically adjust parameters based on signal characteristics. This comprehensive approach results in clearer, more intelligible speech while better preserving original speech properties, ultimately improving the effectiveness and accuracy of speech processing systems. The method typically involves MATLAB functions such as wden for wavelet denoising with custom threshold rules, alongside voice activity detection algorithms for voiced/unvoiced classification.