Toolbox for Speech Signal Processing

This toolbox provides extensive capabilities for speech signal processing, including analysis, processing, and optimization of audio data. It implements multiple algorithms and functions such as spectral analysis for visualizing frequency components, noise reduction techniques using wavelet transforms or spectral subtraction, and speech recognition modules based on MFCC feature extraction and pattern matching. The toolbox enables users to perform frame-based processing with overlap-add methods, implement digital filters for audio enhancement, and extract acoustic features using windowing functions (e.g., Hamming window). Researchers, engineers, and speech enthusiasts can leverage these functionalities to achieve clearer, more natural, and accurate speech signals, facilitating innovation in applications like voice activity detection, speaker identification, and audio restoration. The modular design allows easy integration of custom algorithms while maintaining compatibility with standard audio formats through PCM encoding/decoding routines.