MATLAB Implementation of Speech Signal Processing with Mel Endpoint Detection

In speech signal processing, we can employ multiple methods for noise reduction. One effective approach involves using mel endpoint detection to identify signal endpoints. Mel endpoint detection is a spectrum-based technique that helps determine the start and end points of speech signals. By applying mel endpoint detection after implementing several noise reduction methods, we can significantly improve detection accuracy and enhance the overall quality of speech signal processing. Key implementation aspects include: - Utilizing MATLAB's signal processing toolbox for spectral analysis - Implementing mel-frequency cepstral coefficients (MFCC) for feature extraction - Applying threshold-based algorithms for endpoint detection - Comparing detection performance across different noise reduction techniques like spectral subtraction, Wiener filtering, and wavelet denoising The typical MATLAB workflow involves: 1. Preprocessing the audio signal using audioread() and normalization 2. Applying noise reduction algorithms to clean the signal 3. Computing mel-frequency spectrogram using mfcc() function 4. Implementing energy-based detection with adaptive thresholds 5. Validating endpoints against ground truth labels This integrated approach demonstrates how combining noise reduction with advanced detection methods can optimize speech processing systems for applications like speech recognition and speaker identification.