MATLAB Implementation of Speech Enhancement with Complementary Signal Processing Techniques

This content discusses several fundamental speech signal processing techniques. Speech enhancement is a technique that removes noise from signals to improve speech clarity, often implemented in MATLAB using spectral subtraction or Wiener filtering algorithms that estimate and subtract noise components from the frequency spectrum. Cepstral analysis transforms signals into the cepstral domain where convolutional components become additive, making it easier to separate excitation and vocal tract characteristics - typically achieved using MATLAB's FFT, logarithm, and inverse FFT operations. Endpoint detection identifies the start and end points of speech segments, crucial for applications like speech recognition; common MATLAB implementations use energy thresholds, zero-crossing rates, or combined feature detectors. Formant detection identifies resonant frequencies in speech signals that correlate with pitch and timbre, frequently implemented through linear predictive coding (LPC) or peak-picking algorithms in MATLAB's signal processing toolbox. Finally, noise reduction techniques purify signals by eliminating background noise, with MATLAB implementations ranging from basic filtering to advanced machine learning approaches. These technologies play vital roles in speech signal processing, making understanding their working principles and application scenarios essential for effective implementation.