Popular music analysis: Chorus and emotion Detection

In this paper, a chorus detection and an emotion detection algorithm for popular music are proposed. First, a popular music is decomposed into chorus and verse segments based on its color representation and MFCCs (Mel-frequency cepstral coefficients). Four features including intensity, tempo and rhythm regularity are extracted from these structured segments for emotion detection. The emotion of a song is classified into four classes of emotions: happy, angry, depressed and relaxed via a back-propagation neural network classifier. Experimental results show that the average recall and precision of the proposed chorus detection are approximated to 95% and 84%, respectively; the average precision rate of emotion detection is 88.3% for a test database consisting of 210 popular music songs.