Standard Spectral Subtraction Algorithm and Wavelet Threshold Denoising Method with Implementation Insights

In the field of signal processing, spectral subtraction algorithms and wavelet threshold denoising methods are widely employed. The spectral subtraction algorithm analyzes the signal's frequency spectrum to reduce noise impact, thereby enhancing signal quality through power spectrum estimation and noise spectrum subtraction techniques. The wavelet threshold denoising method utilizes wavelet transform to decompose signals into different frequency sub-bands, applying threshold-based processing to each sub-signal to minimize noise interference using soft or hard thresholding functions. While these methods may not deliver exceptional performance metrics, they serve as fundamental denoising approaches with substantial reference value, featuring prominently in technical literature and providing baseline implementations for performance comparison. Key implementation aspects include proper threshold selection strategies for wavelet methods and spectral floor parameters for spectral subtraction to prevent musical noise artifacts.