What makes beat tracking difficult? A case study on chopin mazurkas

Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010

Volumn , Issue , 2010, Pages 649-654

  Grosche, Peter ^a   Müller, Meinard ^a   Sapp, Craig Stuart ^b  

^a SAARLAND UNIVERSITY   (Germany)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED EXTRACTION; BEAT TRACKING; EVALUATION FRAMEWORK; EXPRESSIVE PERFORMANCE; MUSIC MATERIALS; MUSIC RECORDING; TEST SETS; TRACKING ERRORS;

INFORMATION RETRIEVAL;

ERRORS;

EID: 84873595480     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. The Mazurka Project. http://www.mazurka.org.uk, 2010.
2. MIREX 2009. Audio beat tracking results. http://www.music-ir.org/mirex/ 2009/index.php/Audio-Beat-Tracking-Results 2009.
3. J. P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. E. P. Davies, and M. B. Sandler. A tutorial on onset detection in music signals. IEEE Trans. on Speech and Audio Processing, 13(5):1035-1047, 2005.
4. M. E. P. Davies, N. Degara, and M. D. Plumbley. Evaluation methods for musical audio beat tracking algorithms. Technical Report C4DMTR- 09-06, Queen Mary University, Centre for Digital Music, 2009.
5. M. E. P. Davies and M. D. Plumbley. Context-dependent beat tracking of musical audio. IEEE Trans. on Audio, Speech and Language Processing, 15(3):1009-1020, 2007.
6. S. Dixon. An empirical comparison of tempo trackers. In Proc. of Brazilian Symposium on Computer Music, pages 832-840, 2001.
7. S. Dixon and W. Goebl. Pinpointing the beat: Tapping to expressive performances. In Proc. of International Conference on Music Perception and Cognition, pages 617-620, Sydney, Australia, 2002.
8. A. Earis. An algorithm to extract expressive timing and dynamics from piano recordings. Musicae Scientiae, 11(2), 2007.
9. S. Ewert, M. Müller, and P. Grosche. High resolution audio synchronization using chroma onset features. In Proc. of IEEE ICASSP, Taipei, Taiwan, 2009.
10. F. Gouyon, A. Klapuri, S. Dixon, M. Alonso, G. Tzanetakis, C. Uhle, and P. Cano. An experimental comparison of audio tempo induction algorithms. IEEE Trans. on Speech and Audio Processing, 14, 2006.
11. P. Grosche and M. Müller. A mid-level representation for capturing dominant tempo and pulse information in music recordings. In Proc. of ISMIR, pages 189-194, Kobe, Japan, 2009.
12. A. P. Klapuri, A. J. Eronen, and J. Astola. Analysis of the meter of acoustic musical signals. IEEE Trans. on Audio, Speech and Language Processing, 14(1):342-355, 2006.
13. M. F. McKinney, D. Moelants, M. E. P. Davies, and A. Klapuri. Evaluation of audio beat tracking and music tempo extraction algorithms. Journal of New Music Research, 36(1):1-16, 2007.
14. G. Peeters. Template-based estimation of time-varying tempo. EURASIP Journal on Advances in Signal Processing, 2007.
15. C. S. Sapp. Hybrid numeric/rank similarity metrics. In Proc. of IS- MIR, pages 501-506, Philadelphia, USA, 2008.