Abstract
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.
| Original language | English |
|---|---|
| Pages | 907-910 |
| Number of pages | 4 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 - Biopolis, Singapore Duration: 2010 Dec 14 → 2010 Dec 17 |
Other
| Other | 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 |
|---|---|
| Country/Territory | Singapore |
| City | Biopolis |
| Period | 2010/12/14 → 2010/12/17 |
Keywords
- Chorus
- MFCC
- Music emotion
- Neural network
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
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