@inproceedings{912a02fe2d6a4905a25e9cecda4e8b0b,
title = "The extraction of popular music chorus via structural content analysis",
abstract = "Automatic chorus extraction from popular music is an interesting topic. Chorus extraction facilitates a user to quickly and efficiently preview selections from a large music database and also an essential preprocessing step for further purposes such as indexing and search. In this paper, a framework for extracting chorus from popular music based on structural content analysis is proposed. The repetitive structure is characterized via music color representation called colormap. A colormap of a song based on mapping different frequency bands to color space is used to find repeating patterns. This representation efficiently reveals the relationship of music structure. MFCCs (Mel Frequency Cepstral Coefficients) are employed to identify chorus and verse of a song according the structure information and music domain knowledge. Experimental results show that the performance of the proposed method over a sizable popular song database.",
keywords = "Chorus detection, Colormap, MFCC, Music structure analysis",
author = "Yeh, {Chia Hung} and Lin, {Hung Hsuan}",
year = "2007",
doi = "10.1109/IECON.2007.4460110",
language = "English",
isbn = "1424407834",
series = "IECON Proceedings (Industrial Electronics Conference)",
pages = "2532--2536",
booktitle = "Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON",
note = "33rd Annual Conference of the IEEE Industrial Electronics Society, IECON ; Conference date: 05-11-2007 Through 08-11-2007",
}