Audio Stream Segmentation and Clustering

This codebase implements audio segmentation and clustering functionality, developed by enhancing existing implementations. It demonstrates effective performance in audio stream partitioning and contains comprehensive engineering modules suitable for various speech processing applications. For researchers working on speaker recognition or speech separation tasks, this code provides substantial utility through its implementation of advanced algorithms and techniques. The solution incorporates key audio processing methodologies including Bayesian Information Criterion (BIC) for optimal segmentation points detection, Gaussian Mixture Models (GMM) for speaker clustering, and Mel-Frequency Cepstral Coefficients (MFCC) for robust feature extraction. These components work together to achieve precise segmentation and clustering, significantly improving research accuracy and experimental outcomes. Additionally, the codebase offers extensible features and configurable options, allowing users to customize and expand functionality according to specific requirements. Key functions include audio preprocessing modules, similarity matrix computation, and clustering validation mechanisms. The implementation supports parameter tuning for segmentation sensitivity and cluster number determination. Overall, this represents a powerful and flexible tool for diverse speech processing applications, enabling researchers to obtain superior results in speaker diarization, voice activity detection, and multi-speaker separation scenarios. The modular architecture facilitates easy integration with existing pipelines while maintaining high computational efficiency.