Deep belief networks for automatic music genre classification

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2011, Pages 2433-2436

  Yang, Xiaohong ^a   Chen, Qingcai ^a   Zhou, Shusen ^a   Wang, Xiaolong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Deep belief networks; Modulation spectral analysis; Music genre classification; Timbral texture feature

Indexed keywords

ACOUSTIC FEATURES; CLASSIFICATION METHODS; CONTENT-BASED ANALYSIS; DATA SETS; DEEP BELIEF NETWORKS; FEATURE VECTORS; K-NEAREST NEIGHBORS; LAYER-WISE; LINEAR DISCRIMINANT ANALYSIS; LOCAL OPTIMA; MODEL PARAMETERS; MUSIC GENRE; MUSIC GENRE CLASSIFICATION; MUSIC SIGNALS; RESTRICTED BOLTZMANN MACHINE; TEXTURE FEATURES;

CLASSIFICATION (OF INFORMATION); IMAGE RETRIEVAL; LEARNING ALGORITHMS; SPECTRUM ANALYSIS;

BAYESIAN NETWORKS;

EID: 84865754610     PISSN: None     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Tzanetakis G. and Cook P., "Musical genre classification of audio signals", IEEE Trans. Speech and Audio Processing, 10(5):293-302, 2002.
2. Li, T., Ogihara, M. and Li, Q., "A comparative study on contentbased music genre classification", in Proc. of SIGIR, 282-289, 2003.
3. Li, T. and Ogihara, M., "Toward intelligent music information retrieval", IEEE Trans. Multimedia, 8(3):564-574, 2006.
4. Jang, D., Jin, M.H. and Yoo, C.D., "Music genre classification using novel features and a weighted voting method", in Proc. of ICME, 2008.
5. Song, Y.Q. and Zhang C.S., "Content-based information fusion for semi-supervised music genre classification", IEEE Trans. Multimedia, 10(1):145-152, 2008.
6. Panagakis, Y. and Kotropoulos, C., "Music genre classification via sparse representations of auditory temporal modulations", in Proc. of 17th European Signal Processing Conference, 2009.
7. Lee, C.H., Shih, J.L., Yu, K.M. and Lin, H.S., "Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features", IEEE Trans. Multimedia, 11(4):670-682, 2009.
8. Chang, K.K., Jang, J-S.R. and Iliopoulos, C.S., "Music genre classification via compressive sampling", in Proc. of ISMIR, 387-392, 2010.
9. Panagakis, Y., Kotropoulos, C. and Arce, G.R., "Non-Negative multilinear principal component analysis of auditory temporal modulations for music genre classification", IEEE Trans. Audio, Speech and Language Processing, 18(3):576-588, 2010.
10. Panagakis, Y. and Kotropoulos, C., "Music genre classification via topology preserving non-negative tensor factorization and sparse representations", in Proc. of ICASSP, 249-252, 2010.
11. Benetos, E. and Kotropoulos, C., "Non-negative tensor factorization applied to music genre classification", IEEE Trans. Audio, Speech and Language Processing, 18(8):1955-1967, 2010.
12. Hinton, G.E., Osindero, S. and Teh, Y.W., "A fast learing algorithm for deep belief nets", Neural Computation, 18(7):1527-1554, 2006.
13. Bengio, Y., Lamblin, P., Popovici, D. and Larochelle, H., "Greedy layer-wise training of deep networks. in Proc. of NIPS, 2006.
14. Ballan, L., Bazzica, A., Bertini, M., Bimbo, A.D. and Serra, G., "Deep networks for audio event classification in scoccer videos", in Proc. of ICME, 2009.
15. Lee, H., Largman, Y., Pham, P. and Ng, A.Y., "Unsupervised feature learning for audio classification using convolutional deep belief networks", in Proc. of NIPS, 1-9, 2009.
16. Liu, Y., Zhou, S.S and Chen, Q.C., "Discriminative deep belief networks for visual data classification", Pattern Recognition, 2010.