- Research Article
- Open access
- Published:
Automatic Genre Classification of Musical Signals
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 064960 (2006)
Abstract
We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.
References
Agostini G, Longari M, Pollastri E: Musical instrument timbres classification with spectral features. EURASIP Journal on Applied Signal Processing 2003,2003(1):5–14. 10.1155/S1110865703210118
Aucouturier J-J, Pachet F: Representing musical genre: a state of the art. Journal of New Music Research 2003,32(1):83–93. 10.1076/jnmr.32.1.83.16801
Berenzweig A, Ellis D, Logan B, Whitman B: A large scale evaluation of acoustic and subjective music similarity measures. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR '04), October 2004, Barcelona, Spain
Deshpande H, Singh R, Nam U: Classification of musical signals in the visual domain. Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX '01), December 2001, Limerick, Ireland
Dixon S, Gouyon F, Widmer G: Towards characterisation of music via rhythmic patterns. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR '04), October 2004, Barcelona, Spain
Gouyon F, Dixon S, Pampalk E, Widmer G: Evaluating rhythmic descriptors for musical genre classification. Proceedings of the 25th International AES Conference, June 2004, London, UK
Hellmuth O, Allamanche E, Herre J, Kastner T, Lefebvre N, Wistorf R: Music genre estimation from low level audio features. Proceedings of the 25th International AES Conference, June 2004, London, UK
Lambrou T, Kudumakis P, Speller R, Sandler M, Linney A: Classification of audio signals using statistical features on time and wavelet transform domains. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 6: 3621–3624.
Lidy T, Rauber A: Evaluation of feature extractors and psycho-acoustic transformations for music genre classification. Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 34–41.
Lippens S, Martens JP, De Mulder T, Tzanetakis G: A comparison of human and automatic musical genre classification. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montereal, Quebec, Canada 4: 233–236.
Logan B: Mel-frequency cepstral coefficients for music modeling. Proceedings of the International Conference on Music Information Retrieval (ISMIR '00), October 2000, Plymouth, Mass, USA
Lu L, Zhang H-J, Jiang H: Content analysis for audio classification and segmentation. IEEE Transactions on Speech and Audio Processing 2002,10(7):504–516. 10.1109/TSA.2002.804546
McKay C, Fiebrink R, McEnnis D, Li B, Fujinaga I: ACE: a framework for optimizing music classification. Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK
Pachet F, Casaly D: A taxonomy of musical genres. Proceedings of the 6th Conference on Content-Based Multimedia Information Access (RIAO '00), April 2000, Paris, France
Pampalk E: Computational models of music similarity and their application to music information retrieval, Doctoral thesis.
Pampalk E, Flexer A, Widmer G: Improvements of audio-based music similarity and genre classification. Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 628–633.
Pohle T, Pampalk E, Widmer G: Evaluation of frequently used audio features for classification of music into perceptual categories. Proceedings of the 4th International Workshop on Content-Based Multimedia Indexing (CBMI '05), June 2005, Riga, Latvia
Pye D: Content-based methods for the management of digital music. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 4: 2437–2440.
Scheirer E, Slaney M: Construction and evaluation of a robust multifeature speech/music discriminator. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '97), April 1997, Munich, Germany 2: 1331–1334.
Thiede TV: Perceptual audio quality assessment using a non-linear filter bank, Ph.D. thesis. Technical University of Berlin, Berlin, Germany; 1999.
Tzanetakis G, Cook P: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 2002,10(5):293–302. 10.1109/TSA.2002.800560
West K, Cox S: Features and classifiers for the automatic classification of musical audio signals. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR '04), October 2004, Barcelona, Spain
Wold E, Blum T, Keislar D, Wheaton J: Content-based classification, search, and retrieval of audio. IEEE Multimedia 1996,3(3):27–36. 10.1109/93.556537
Xu C, Maddage NC, Shao X: Automatic music classification and summarization. IEEE Transactions on Speech and Audio Processing 2005,13(3):441–450.
2005 MIREX Contest Results - Audio Genre Classification, https://doi.org/www.music-ir.org/evaluation/mirex-results/audio-genre/index.html
ISMIR 2004 Magnatune Genre Classification Training Set, https://doi.org/ismir2004.ismir.net/
ISMIR 2004 Contest Results - Audio Genre Classification, https://doi.org/ismir2004.ismir.net/genre_contest/results.htm
Author information
Authors and Affiliations
Corresponding author
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.
About this article
Cite this article
Barbedo, J.G.s., Lopes, A. Automatic Genre Classification of Musical Signals. EURASIP J. Adv. Signal Process. 2007, 064960 (2006). https://doi.org/10.1155/2007/64960
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1155/2007/64960