Abstract
Audio signal classification is usually done using conventional signal features such as mel-frequency cepstrum coefficients (MFCC), line spectral frequencies (LSF), and short time energy (STM). Learned dictionaries have been shown to have promising capability for creating sparse representation of a signal and hence have a potential to be used for the extraction of signal features. In this paper, we consider to use sparse features for audio classification from music and speech data. We use the K-SVD algorithm to learn separate dictionaries for the speech and music signals to represent their respective subspaces and use them to extract sparse features for each class of signals using Orthogonal Matching Pursuit (OMP). Based on these sparse features, Support Vector Machines (SVM) are used for speech and music classification. The same signals were also classified using SVM based on the conventional MFCC coefficients and the classification results were compared to those of sparse coefficients. It was found that at lower signal to noise ratio (SNR), sparse coefficients give far better signal classification results as compared to the MFCC based classification.