Skip to main content
  • Research Article
  • Open access
  • Published:

Multiple Scale Music Segmentation Using Rhythm, Timbre, and Harmony

Abstract

The segmentation of music into intro-chorus-verse-outro, and similar segments, is a difficult topic. A method for performing automatic segmentation based on features related to rhythm, timbre, and harmony is presented, and compared, between the features and between the features and manual segmentation of a database of 48 songs. Standard information retrieval performance measures are used in the comparison, and it is shown that the timbre-related feature performs best.

References

  1. Andersen TH: Mixxx: towards novel dj interfaces. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME '03), May 2003, Montreal, Quebec, Canada 30–35.

    Google Scholar 

  2. Murphy D: Pattern play. In Additional Proceedings of the 2nd International Conference on Music and Artificial Intelligence, September 2002, Edinburgh, Scotland Edited by: Smaill A.

    Google Scholar 

  3. Bartsch MA, Wakefield GH: To catch a chorus: using chroma-based representations for audio thumbnailing. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, October 2001, New Paltz, NY, USA 15–18.

    Google Scholar 

  4. Foote J: Visualizing music and audio using self-similarity. Proceedings of the 7th ACM International Multimedia Conference & Exhibition, November 1999, Orlando, Fla, USA 77–80.

    Google Scholar 

  5. Foote J: Automatic audio segmentation using a measure of audio novelty. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '00), July-August 2000, New York, NY, USA 1: 452–455.

    Article  Google Scholar 

  6. Cooper M, Foote J: Summarizing popular music via structural similarity analysis. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '03), October 2003, New Paltz, NY, USA 127–130.

    Google Scholar 

  7. Jensen K: A causal rhythm grouping. Proceedings of 2nd International Symposium on Computer Music Modeling and Retrieval (CMMR '04), 2005, Lecture Notes in Computer Science 3310: 83–95.

    Article  Google Scholar 

  8. Peeters G, Rodet X: Signal-based music structure discovery for music audio summary generation. Proceedings of International Computer Music Conference (ICMC '03), Octobre 2003, Singapore 15–22.

    Google Scholar 

  9. Dannenberg RB, Hu N: Pattern discovery techniques for music audio. Journal of New Music Research 2003,32(2):153–163. 10.1076/jnmr.32.2.153.16738

    Article  Google Scholar 

  10. Goto M: A chorus-section detecting method for musical audio signals. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '03), April 2003, Hong Kong 5: 437–440.

    Google Scholar 

  11. Dubnov S, Assayag G, El-Yaniv R: Universal classification applied to musical sequences. Proceedings of the International Computer Music Conference (ICMC '98), October 1998, Ann Arbor, Mich, USA 332–340.

    Google Scholar 

  12. Jehan T: Hierarchical multi-class self similarities. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '05), October 2005, New Paltz, NY, USA 311–314.

    Google Scholar 

  13. Jensen K, Xu J, Zachariasen M: Rhythm-based segmentation of popular chinese music. Proceedings of 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 374–380.

    Google Scholar 

  14. Hermansky H: Perceptual linear predictive (PLP) analysis of speech. Journal of the Acoustical Society of America 1990,87(4):1738–1752. 10.1121/1.399423

    Article  Google Scholar 

  15. Jensen K: Perceptual atomic noise. Proceedings of the International Computer Music Conference (ICMC '05), September 2005, Barcelona, Spain 668–671.

    Google Scholar 

  16. Collins N: A comparison of sound onset detection algorithms with emphasis on psychoacoustically motivated detection functions. Proceedings of AES 118th Convention, May 2005, Barcelona, Spain

    Google Scholar 

  17. Desain P: A (de)composable theory of rhythm. Music Perception 1992,9(4):439–454.

    Article  Google Scholar 

  18. Sekey A, Hanson BA: Improved 1-bark bandwidth auditory filter. Journal of the Acoustical Society of America 1984,75(6):1902–1904. 10.1121/1.390954

    Article  Google Scholar 

  19. Eckmann JP, Kamphorst SO, Ruelle D: Recurrence plots of dynamical systems. Europhysics Letters 1987,4(9):973–977. 10.1209/0295-5075/4/9/004

    Article  Google Scholar 

  20. Cormen TH, Stein C, Rivest RL, Leiserson CE: Introduction to Algorithms. 2nd edition. The MIT Press, Cambridge, UK; McGraw-Hill, New York, NY, USA; 2001.

    MATH  Google Scholar 

  21. Tzanetakis G, Cook P: Multifeature audio segmentation for browsing and annotation. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '99), October 1999, New Paltz, NY, USA 103–106.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristoffer Jensen.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Jensen, K. Multiple Scale Music Segmentation Using Rhythm, Timbre, and Harmony. EURASIP J. Adv. Signal Process. 2007, 073205 (2006). https://doi.org/10.1155/2007/73205

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/73205

Keywords