An audio feature extraction scheme based on spectral decomposition

This paper presents an audio feature extraction scheme based on spectral decomposition where the decomposition is performed iteratively by matching-pursuit in frequency domain. Motivated by psychoacoustic studies, a set of spectral basis vectors are constructed to extract pitch, timbre and residual inharmonic components from spectrum. The audio feature is represented by the scales of basis vectors after dimension reduction. The proposed feature is evaluated on a 13-category audio effect classification task. The experimental results show the proposed feature outperforms other spectral and cepstral features.