Speech Denoising Based on Wavelet Packet Decomposition

In this paper, we employ a speech denoising algorithm based on wavelet packet decomposition to handle noise in real-world environments. The core algorithm utilizes entropy spectral probability density function for threshold estimation to achieve denoising effects. Wavelet packet decomposition serves as an effective signal analysis method that decomposes speech signals into sub-waves representing different frequency components. Through threshold processing of these sub-waves, we can selectively remove noise while preserving critical information in the speech signal. From an implementation perspective, the algorithm typically involves three main steps: First, performing wavelet packet decomposition using functions like wpdec() in MATLAB to obtain detailed frequency components. Second, calculating the entropy-based threshold by analyzing the probability density distribution of the wavelet coefficients. Finally, applying threshold processing functions such as wthresh() to modify the coefficients and reconstruct the signal using wprec(). Therefore, the wavelet packet decomposition-based speech denoising algorithm proves to be highly effective in practical applications, significantly improving speech clarity and quality by maintaining the balance between noise removal and signal preservation.