On the importance of modeling temporal information in music tag annotation

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

Volumn , Issue , 2009, Pages 1873-1876

  Reed, Jeremy ^a   Lee, Chin Hui ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Hidden Markov models; Information retrieval; Music; Speech processing; Vector quantization

Indexed keywords

AUTOMATIC TAGGING; BAUM-WELCH; ITERATIVE PROCESS; LATENT SEMANTIC ANALYSIS; MUSIC; MUSIC INFORMATION RETRIEVAL; SEGMENT MODELS; STATISTICAL MODELS; TEMPORAL ASPECTS; TEMPORAL INFORMATION; VITERBI DECODING;

ACOUSTICS; COMPUTATIONAL GRAMMARS; COMPUTER MUSIC; ELECTRONIC MUSICAL INSTRUMENTS; HIDDEN MARKOV MODELS; IMAGE RETRIEVAL; INFORMATION SERVICES; ITERATIVE DECODING; SIGNAL PROCESSING; SPEECH PROCESSING; VECTORS; VITERBI ALGORITHM;

VECTOR QUANTIZATION;

EID: 70349195978     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2009.4959973     Document Type: Conference Paper

References (13)

1. D. Turnbull, L. Barrignton, D. Torres, and G. Lanckriet, "Semantic annotation and retrieval of music and sound effects," IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 2, pp. 467-476, 2008.
2. J. Bellegarda, "Exploiting latent semantic information in statistical language modeling," Proc. IEEE, vol. 88, no. 8, pp. 1279-1296, 2000.
3. B. Whitman, Learning the meaning of music, Ph.D. thesis, Mass. Inst. Technol., 2005.
4. M. Slaney, "Semantic-audio retrieval," in Proceedings of ICASSP, August 2002, pp. 4108-4111.
5. M. Casey and M. Slaney, "The importance of sequences in musical similarity," in Proceedings of ICASSP, May 2006, pp. V5-V8.
6. J. Reed and C.-H. Lee, "A study on music genre classification based on universal acoustic models," in Proceedings of Intern. Symp. of Music Info. Retrieval (ISMIR), October 2006, pp. 89-94.
7. J.-J. Aucouturier and F. Pachet, "Representing music genre: A state of the art," J. New Music Research, vol. 32, no. 1, pp. 83-93, 2003.
8. T. Svendsen and F. Soong, "On the automatic segmentation of speech signals," in Proceedings of ICASSP, April 1987, pp. 77-80.
9. A. Meng and J. Shawe-Taylor, "An investigation of feature models for music genre classification using support vector classifier," in Proceedings of Intern. Symp. of Music Info. Retrieval (ISMIR), September 2005, pp. 604-609.
10. T. Joachims, Making large-scale SVM learning practical, pp. 10-119, Advances in Kernel Methods - Support Vector Learning. MIT-Press, 1999.
11. T. G. Dietterich, "Approximate statistical tests for comparing supervised classification learning algorithms," Neural Computation, vol. 10, pp. 1895-1923, 1998.
12. M. Zissman, "Comparison of four approaches to automatic language identification of telephone speech," IEE Trans. Speech, and Audio Process., vol. 4, no. 1, pp. 31-44, 1996.
13. M. A. Bartsh and G. H. Wakefield, "To catch a chorus: Using chroma-based representations for audio thumbnailing," in Proc, IEEE Workshop on Appl. of Signal Process. to Audio and Acoustics, 2001, pp. 15-18.