Wiener Speech Denoising with DD Algorithm-Based Priori SNR Estimation

The complete Wiener speech denoising program based on DD algorithm for priori SNR estimation involves the following processing steps: 1. Speech Framing: The input speech signal is divided into short-time frames using overlapping windowing techniques (typically Hamming or Hanning windows) to facilitate time-frequency analysis. This segmentation allows for stationary processing of quasi-stationary speech signals. 2. Dynamic SNR Estimation: The DD (Decision-Directed) algorithm performs frame-by-frame SNR estimation by calculating the ratio between speech power spectral density and noise power spectral density. This approach combines previous SNR estimates with current instantaneous SNR measurements to provide smooth, reliable SNR tracking while minimizing musical noise artifacts. 3. Noise Estimation Update: The noise estimation model dynamically adapts based on real-time SNR analysis, employing minimum statistics or voice activity detection to distinguish noise-dominated frames from speech-active frames. This adaptive update mechanism ensures robust performance in varying acoustic environments. 4. Frame Reconstruction: Each frame undergoes Wiener filtering using the estimated SNR parameters and noise models. The frequency-domain filtering applies gain factors derived from the SNR estimates to attenuate noise components while preserving speech characteristics, followed by overlap-add synthesis to reconstruct the continuous denoised speech signal. This implementation represents a comprehensive solution that effectively reduces background noise and enhances speech quality through systematic signal processing techniques and adaptive parameter control.