Genre classification and the invariance of MFCC features to key and tempo

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6523 LNCS, Issue PART 1, 2011, Pages 317-327

  Li, Tom L H ^a   Chan, Antoni B ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFIER TRAINING; GENRE CLASSIFICATION; MUSICAL GENRE; MUSICAL GENRE CLASSIFICATION; MUSICAL INFORMATION RETRIEVAL; POOR PERFORMANCE; SHORT DURATIONS;

CLASSIFIERS; ENCODING (SYMBOLS); INFORMATION RETRIEVAL; TEXTURES;

NATURAL LANGUAGE PROCESSING SYSTEMS; CLASSIFICATION (OF INFORMATION);

EID: 78751660647     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17832-0_30     Document Type: Conference Paper

References (16)

1. Audacity, the free, cross-platform sound editor, http://audacity. sourceforge. net/
2. Bergstra, J., Casagrande, N., Erhan, D., Eck, D., Ḱegl, B.: Aggregate features and Adaboost for music classification. Machine Learning 65(2), 473-484 (2006
3. Bridle, J.S., Brown, M.D.: An experimental automatic word recognition system. JSRU Report, 1003 (1974
4. Computer audition toolbox, http://cosmal.ucsd.edu/cal/projects/catbox/ catbox.htm
5. Cover, T.M., Thomas, J.A.: Elements of information theory. John Wiley and Sons, Chichester (2006
6. Dempster, A.P., Laird, N.M., Rubin, D.B., et al.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1-38 (1977
7. Ellis, D.: Classifying music audio with timbral and chroma features. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 339-340 (2007
8. Li, T., Tzanetakis, G.: Factors in automatic musical genre classification of audio signals. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 143-146 (2003
9. Lu, L., Zhang, H.J., Li, S.Z.: Content-based audio classification and segmentation by using support vector machines. Multimedia Systems 8(6), 482-492 (2003
10. Mandel, M., Ellis, D.: Song-level features and support vector machines for music classification. In: Proc. ISMIR, pp. 594-599. Citeseer (2005
11. Meng, A., Ahrendt, P., Larsen, J.: Improving music genre classification by short time feature integration. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. 5 (2005
12. Pachet, F., Aucouturier, J.J.: Improving timbre similarity: How high is the sky?. Journal of negative results in speech and audio sciences 1(1) (2004
13. Pearce, D., Hirsch, H.G.: The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In: Sixth International Conference on Spoken Language Processing. Citeseer (2000
14. Simard, P.Y., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. In: International Conference on Document Analysis and Recogntion (ICDAR), pp. 958-962. IEEE Computer Society, Los Alamitos (2003
15. Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on speech and audio processing 10(5), 293-302 (2002
16. Verhelst, W., Roelands, M.: An overlap-add technique based on waveform similarity (WSOLA) for high quality time-scale modification of speech. In: IEEE International Conference on Acoustic Speech and Signal Processing, vol. 2, Institute of Electrical Engineers Inc., (IEE) (1993