An Effective Method on Content Based Music Feature Extraction

Based on the theories of frequency domain and time domain signal processing, wavelet analysis, and singular value decomposition (SVD), an effective method for content based music feature extraction is proposed in this paper. Music feature can be divided into three parts by this method, which are frequency feature, auditory perceptual feature, and statistical characteristic of beat. The characteristic of each music can be well described by these features. The results of logistic regression classification model and linear support vector machine (SVM) classification model which is on a data set consists of several different styles of music and use the feature extraction method in this paper show the high precision of 95.33% in average, and also prove the effectiveness of the proposed method. Feature extraction is the foundation of content based recommendation, retrieval, classification, and cluster. Hence this method has good prospect in these area.