A novel time-frequency feature extraction for movie audio signals classification

Most of short time-frequency feature (TFF) extraction methods in the literature only consider scale and frequency of the selected atoms, which neglects the effect of expansion coefficient and time of the selected atoms. In order to classify movie audio signals better, an effective and flexible time-frequency feature extraction method using expansion coefficient, scale, time and frequency of the selected atoms is investigated in this work, which consists of four stages: signal decomposition, Wigner-Ville distribution, principal component extraction and clustering. The experimental results show that the proposed TFF is better than the traditional TFF, which can improve 6% in accuracy for classifying twenty kinds of movie audio signals. The best dimension number of the proposed TFF is 25.