A statistical approach to musical genre classification using non-negative matrix factorization

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 2007, Pages

  Holzapfel, André ^a   Stylianou, Yannis ^a  

^a UNIVERSITY OF CRETE   (Greece)

Author keywords

Gaussian mixture model; MFCC; Music genre classification; Non negative matrix factorization

Indexed keywords

GAUSSIAN MIXTURE MODELS; MUSIC GENRE CLASSIFICATION; NON-NEGATIVE MATRIX FACTORIZATION;

CLASSIFICATION (OF INFORMATION); FACTORIZATION; FEATURE EXTRACTION; MAXIMUM LIKELIHOOD ESTIMATION; STATISTICAL METHODS;

SPEECH PROCESSING;

EID: 34547514190     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2007.366330     Document Type: Conference Paper

References (13)

1. Bob Snyder, Music and Memory, Cambridge, MIT Press, 2000.
2. http://www.magnatune.com
3. Michael I. Mandel and Daniel P.W. Ellis, Song-level, features and support vector machines for music classification, 6th International ISMIR 2005 Conference, London, UK, 2005.
4. Francois Pachet and Jean-Julien Aucouturier, Improving Timbre Similarity: How high is the sky?. Journal of negative results in speech and audio sciences, vol., 1.1, 2004.
5. Beiming Wang and Mark D. Plumbley, Musical audio stream separation by nonnegative matrix factorization, DMRN Summer Conference, Glasgow, UK, 2005.
6. Emmanouil Benetos and Margarita Kotti and Constantine Kotropoulos, Musical instrument classification using Non-negative Matrix Factorization algorithms and subset feature selection, ICASSP, Toulouse, 2006.
7. Daniel D. Lee and H. Sebastian Seung, Algorithms for non-negative matrix factorization, Advances in Neural Information Processing Systems, vol.13, 2001, pp. 556-562.
8. L. Baum and J. Eagon, An inequality with applications to statistical estimation for probalistic functions of Markov processes and to a model for ecology, American Mathematical Society Bulletin, 73, 1967, pp. 360-363.
9. Greg Hamerly and Charles Elkan, Learning the k in kmeans, 17th Annual Conference on Neural Information Processing Systems (NIPS), 2003, pp. 281-288.
10. Tao Li. and Georgos Tzanetakis, Factors in automatic musical genre classification of audio signals, IEEE Workshop on Applications of Signal. Processing to Audio and Acoustics, New Paltz, NY, 2003.
11. James Bergstra and Norman Casagrande and Dumitru Erhan and Douglas Eck and Balász Kégl, Aggregate features and ADABOOST for music classification, Kluwer Academic Publishers, 2006.
12. Elias Pampalk, A matlab toolbox to compute music similarity from audio, 5th International ISMIR 2004 Conference, Barcelona, Spain, 2004.
13. West, K. and Cox, S., 2004, Features and Classifiers for the automatic classification of musical audio signals, 5th International ISMIR 2004 Conference, Barcelona, Spain, 2004.