Dual-Channel Speech Enhancement Algorithm

We have developed a dual-channel speech enhancement algorithm designed to eliminate environmental noise and improve speech clarity and intelligibility. This algorithm employs a normalized least mean squares (NLMS) adaptive filtering approach, which dynamically adjusts filter coefficients based on real-time noise characteristics. The implementation involves cross-correlation analysis between primary and reference microphone signals to separate noise components from the target speech signal. Through this method, the algorithm effectively reduces noise levels, achieving 10dB of noise cancellation while preserving speech quality. The core processing includes frame-based audio segmentation, Fast Fourier Transform (FFT) analysis, and adaptive filter coefficient updates using a normalized step-size parameter for stable convergence. By utilizing this algorithm, speech quality is significantly improved, enabling users to hear conversational content more clearly in noisy environments. Our objective is to provide a high-quality, reliable speech enhancement solution that meets communication requirements in challenging acoustic conditions.