Evolving Automatically High-Level Music Descriptors from Acoustic Signals
High-Level music descriptors are key ingredients for music information retrieval systems. Although there is a long tradition in extracting information from acoustic signals, the field of music information extraction is largely heuristic in nature. We present here a heuristic-based generic approach for extracting automatically high-level music descriptors from acoustic signals. This approach is based on Genetic Programming, that is used to build extraction functions as compositions of basic mathematical and signal processing operators. The search is guided by specialized heuristics that embody knowledge about the signal processing functions built by the system. Signal processing patterns are used in order to control the general function extraction methods. Rewriting rules are introduced to simplify overly complex expressions. In addition, a caching system further reduces the computing cost of each cycle. In this paper, we describe the overall system and compare its results against traditional approaches in musical feature extraction à la Mpeg7.
Unable to display preview. Download preview PDF.
- 2.Scheirer, E.D., Slaney, M.: Construction and evaluation of a robust multifeature speech/music discriminator. In: Proc ICASSP 1997, pp. 1331–1334Google Scholar
- 3.Herrera, P., Yeterian, A., Gouyon, F.: Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques. In: Proceedings of 2nd International Conference on Music and Artificial Intelligence, Edinburgh, Scotland (2002)Google Scholar
- 4.Peeters, G., Rodet, X.: Automatically selecting signal descriptors for sound classification. In: Proceedings of the 2002 ICMC, Goteborg, Sweden (September 2002)Google Scholar
- 5.Herrera, P., Serra, X., Peeters, G.: Audio descriptors and descriptors schemes in the context of MPEG-7. In: Proceedings of the 1999 ICMC, Beijing, China (October 1999)Google Scholar
- 6.Berenzweig, A.L., Ellis, D.P.W.: Locating singing voice segments within music signals. In: IEEE workshop on applications of signal processing to acoustics and audio (WASPAA 2001), Mohonk NY (October 2001)Google Scholar
- 7.Chou, W., Gu, L.: Robust Singing Detection in Speech/Music Discriminator Design. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001), Salt Lake City, Utah, USA, pp. 865–868 (May 2001)Google Scholar
- 8.Aucouturier, J.J., Pachet, F.: Music similarity measures: what’s the use? In: proceedings of the 3rd international symposium on music information retrieval (ISMIR 2002), Paris (October 2002)Google Scholar
- 9.Tzanetakis, G., Essl, G., Cook, P.: Automatic musical genre classification of audio signals. In: Proceedings of 2nd International Symposium on Music Information Retrieval, Bloomington, IN, USA, pp. 205–210 (October 2001)Google Scholar
- 10.Koza, J.R.: Genetic Programming: on the programming of computers by means of natural selection. The MIT Press, CambridgeGoogle Scholar
- 11.Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3-2, 199–230 (1995)Google Scholar