A Novel Method Based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification

Music mood is useful for music-related applications such as music retrieval or recommendation, which represents the inherent emotional expression of music signals. In this paper, a novel technique is proposed for music signal analysis in the view of emotions, which is based on the orthogonal matching pursuit, Gabor functions, and the Wigner distribution function. The technique, called the OMPGW method, consists of three-level schemes: the low-level, the middle-level and the high-level schemes. For the low-level schemes, the orthogonal matching pursuit combined with Gabor functions is proposed to provide an adaptive time-frequency decomposition of music signals. Compared with other algorithms for signal analysis, the proposed algorithm can achieve a higher spatial and temporal resolution and give a better interpret of the music signal structures. In the middle-level schemes, the Wigner distribution function is applied to obtain the time-frequency energy distribution of the results from the low-level schemes. High-level schemes are used to describe the modeling of audio features, the procedure of music mood classification. A classifier based on support vector machines is utilized to model the extracted features with the proposed technique regarding the emotion models. Several experiments are conducted with four datasets, and better results are achieved with the proposed method. In music mood classification experiments, music clips are classified into different kinds of mood clusters, and mean accuracy of 69.53 percent on our dataset can be achieved using the OMPGW method.