Various SVM Classification Algorithms for Audio Classification

This repository presents a comprehensive collection of various Support Vector Machine (SVM) classification algorithms specifically designed for audio classification tasks. Audio classification represents a significant computational challenge, where these algorithms enable systematic organization and understanding of large-scale audio datasets through effective pattern recognition. The implementation includes multiple SVM variants such as linear SVM, kernel-based SVM (RBF, polynomial), and multi-class classification approaches using one-vs-rest or one-vs-one strategies.

These algorithms demonstrate wide applicability across multiple domains including speech recognition (featuring MFCC extraction and spectral analysis), music genre classification (utilizing temporal and frequency-domain features), and voiceprint identification (employing speaker-specific characteristics). The code structure typically involves feature extraction modules using libraries like LibROSA, followed by SVM model training with scikit-learn's SVC class, incorporating parameter optimization through grid search and cross-validation techniques.

Mastering these classification algorithms is crucial for both academic research and practical applications in audio processing, as they provide robust solutions for handling high-dimensional audio features while maintaining generalization capabilities through proper kernel selection and regularization parameter tuning.