MATLAB Program for Sound Signal Recognition Using Support Vector Machine (SVM)

Support Vector Machine (SVM) is a powerful machine learning algorithm particularly suitable for classification tasks involving sound signals. In sound recognition applications, SVM achieves efficient classification by extracting discriminative acoustic features. This MATLAB program employs Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC) to characterize sound signals, followed by SVM model training for classification. Mel-Frequency Cepstral Coefficients (MFCC) represent a widely-used feature extraction method in speech signal processing that mimics human auditory perception of frequency, effectively capturing critical spectral information. The implementation involves calculating logarithmic mel-scale filterbank energies and applying discrete cosine transformation to decorrelate the features. Meanwhile, Linear Predictive Cepstral Coefficients (LPCC) extract formant characteristics through linear predictive analysis, which models the vocal tract using autoregressive coefficients. The combination of these two feature sets provides more comprehensive and robust feature vectors, thereby improving classification accuracy. In the MATLAB implementation, the program first preprocesses input sound signals through framing (typically using 20-40ms frames), windowing (Hamming window application to reduce spectral leakage), and Fast Fourier Transform (FFT) for spectral analysis. The code then computes MFCC features through mel-filterbank processing and LPCC features via linear prediction coding analysis. These features are concatenated into high-dimensional feature vectors using horizontal stacking (horzcat function in MATLAB) and normalized before SVM training. The program utilizes MATLAB's Classification Learner app or fitcsvm function for model training with customizable kernel functions (linear, RBF, or polynomial). The trained model can classify new sound signals through the predict function, and the complete code framework includes modular functions for easy extension and optimization. This methodology applies to various sound recognition scenarios including speech recognition, environmental sound classification, and bioacoustic analysis. Model performance can be further enhanced by tuning SVM hyperparameters (like box constraint and kernel scale) through cross-validation or optimizing feature extraction parameters (frame size, number of coefficients) using grid search techniques.