Collection of Various SVM Classification Algorithms for Audio Processing

In this article, I would like to emphasize that we can utilize various self-collected Support Vector Machine (SVM) classification algorithms to implement audio classification. These algorithms can be applied across multiple domains such as speech recognition, music categorization, and sound analysis. Through SVM algorithms, we can extract meaningful features from audio data using techniques like MFCC (Mel-Frequency Cepstral Coefficients) extraction and apply them to classification tasks. Implementation typically involves preprocessing audio signals, feature dimension reduction using PCA, and training SVM models with kernels like RBF or linear classifiers. The applications of these algorithms are extensive, helping us better understand and process audio data efficiently. Therefore, we should recognize the significant role of SVM classification algorithms in audio processing and actively explore their application to improve classification accuracy and computational efficiency through optimized hyperparameter tuning and cross-validation techniques.