Audio-based music structure analysis

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

Volumn , Issue , 2010, Pages 625-636

  Paulus, Jouni ^a   Müller, Meinard ^b   Klapuri, Anssi ^c  

^a FRAUNHOFER INSTITUTE FOR INTEGRATED CIRCUITS IIS   (Germany)

^b SAARLAND UNIVERSITY   (Germany)

^c QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AUDIO-BASED; MUSIC STRUCTURE ANALYSIS; TEMPORAL SEGMENTS;

INFORMATION RETRIEVAL;

AUDIO ACOUSTICS;

EID: 84863552197     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (80)

1. S. Abdallah, K. Nolad, M. Sandler, M. Casey, and C. Rhodes. Theory and evaluation of a Bayesian music structure extractor. In Proc. of 6th International Conference on Music Information Retrieval, pages 420-425, London, England, UK, Sept. 2005.
2. S. Abdallah, M. Sandler, C. Rhodes, and M. Casey. Using duration models to reduce fragmentation in audio segmentation. Machine Learning, 65(2-3):485-515, Dec. 2006.
3. J.-J. Aucouturier and F. Pachet. Improving timbre similarity: How high's the sky. Journal of Negative Results in Speech and Audio Sciences, 1, 2004.
4. J.-J. Aucouturier, F. Pachet, and M. Sandler. "The way it sounds": Timbre models for analysis and retrieval of music signals. IEEE Transactions on Multimedia, 7(6):1028-1035, Dec. 2005.
5. J.-J. Aucouturier and M. Sandler. Segmentation of musical signals using hidden Markov models. In Proc. of 110th Audio Engineering Society Convention, Amsterdam, The Netherlands, May 2001.
6. J.-J. Aucouturier and M. Sandler. Finding repeating patterns in acoustic musical signals: Applications for audio thumbnailing. In Proc. of Audio Engineering Society 22nd International Conference on Virtual, Synthetic and Entertainment Audio, pages 412-421, Espoo, Finland, 2002.
7. L. Barrington, A. B. Chan, and G. Lanckriet. Modeling music as a dynamic texture. IEEE Transactions on Audio, Speech, and Language Processing, 18(3):602-612, Mar. 2010.
8. M. A. Bartsch and G. H. Wakefield. Audio thumbnailing of popular music using chroma-based representations. IEEE Transactions on Multimedia, 7(1):96-104, Feb. 2005.
9. J. P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. Davies, and M. B. Sandler. A tutorial on onset detection in music signals. IEEE Transactions on Speech and Audio Processing, 13(5):1035-1047, Sept. 2005.
10. M. J. Bruderer, M. McKinney, and A. Kohlrausch. Structural boundary perception in popular music. In Proc. of 7th International Conference on Music Information Retrieval, pages 198-201, Victoria, B.C., Canada, Oct. 2006.
11. M. Casey and M. Slaney. The importance of sequences in musical similarity. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 5-8, Toulouse, France, May 2006.
12. A. T. Cemgil, B. Kappen, P. Desain, and H. Honing. On tempo tracking: Tempogram representation and kalman filtering. Journal of New Music Research, 28(4):259-273, 2001.
13. W. Chai. Automated Analysis of Musical Structure. PhD thesis, Massachusetts Institute of Technology, Boston, Mass., USA, Sept. 2005.
14. H.-T. Cheng, Y.-H. Yang, Y.-C. Lin, and H. H. Chen. Multimodal structure segmentation and analysis of music using audio and textual information. In Proc. of IEEE International Symposium on Circuits and Systems, pages 1677-1680, Taipei, Taiwan, May 2009.
15. M. Cooper and J. Foote. Summarizing popular music via structural similarity analysis. In Proc. of 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pages 127-130, New Platz, N.Y., USA, Oct. 2003.
16. R. B. Dannenberg and M. Goto. Music structure analysis from acoustic signals. In D. Havelock, S. Kuwano, and M. Vorländer, editors, Handbook of Signal Processing in Acoustics, volume 1, pages 305- 331. Springer, New York, N.Y., USA, 2008.
17. R. B. Dannenberg and N. Hu. Pattern discovery techniques for music audio. In Proc. of 3rd International Conference onMusic Information Retrieval, pages 63-70, Paris, France, Oct. 2002.
18. S. B. Davis and P. Mermelstein. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. Readings in Speech Recognition, pages 65-74, 1990.
19. S. Dixon, E. Pampalk, and G. Widmer. Classification of dance music by periodicity patterns. In Proc. of 4th International Conference on Music Information Retrieval, pages 159-165, Baltimore, Md., USA, Oct. 2003.
20. J. S. Downie. The music information retrieval evaluation exchange (2005-2007): A window into music information retrieval research. Acoustical Science and Technology, 29(4):247-255, 2008.
21. J.-P. Eckmann, S. O. Kamphorst, and D. Ruelle. Recurrence plots of dynamical systems. Europhysics Letters, 4(9):973-977, Nov. 1987.
22. D. P. W. Ellis. Beat tracking by dynamic programming. Journal of New Music Research, 36(1):51-60, 2007.
23. A. Eronen. Chorus detection with combined use of MFCC and chroma features and image processing filters. In Proc. of 10th International Conference on Digital Audio Effects, pages 229-236, Bordeaux, France, Sept. 2007.
24. J. Foote. Visualizing music and audio using self-similarity. In Proc. of ACM Multimedia, pages 77-80, Orlando, Fla., USA, 1999.
25. J. Foote. Automatic audio segmentation using a measure of audio novelty. In Proc. of IEEE International Conference on Multimedia and Expo, pages 452-455, New York, N.Y., USA, Aug. 2000.
26. J. Foote and S. Uchihashi. The beat spectrum: A new approach to rhythm analysis. In Proc. of IEEE International Conference on Multimedia and Expo, pages 881-884, Tokyo, Japan, Aug. 2001.
27. S. Gao, N. C. Maddage, and C.-H. Lee. A hidden Markov model based approach to music segmentation and identification. In Proc. of 4th Pacific Rim Conference on Multimedia, pages 1576-1580, Singapore, Dec. 2003.
28. J. Goldberger, S. Gordon, and H. Greenspan. An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures. In Proc. of Ninth IEEE International Conference on Computer Vision, pages 487-493, 2003.
29. E. Gómez. Tonal Description of Music Audio Signals. PhD thesis, UPF Barcelona, 2006.
30. M. M. Goodwin and J. Laroche. A dynamic programming approach to audio segmentation and music/speech discrimination. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 309-312, Montreal, Que., Canada, May 2004.
31. M. Goto. A chorus-section detecting method for musical audio signals. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 437-440, Hong Kong, 2003.
32. P. Grosche and M. Müller. A mid-level representation for capturing dominant tempo and pulse information in music recordings. In Proc. of 10th International Conference on Music Information Retrieval, pages 189-194, Kobe, Japan, Oct. 2009.
33. P. Grosche, M. Müller, and F. Kurth. Cyclic tempogram a mid-level tempo representation for music signals. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, Dallas, Texas, USA, Mar. 2010.
34. L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, 2(1):193-218, Dec. 1985.
35. International Organization for Standardization. ISO/IEC 15938- 4:2002 Information technology - Multimedia content description interface - Part 4: Audio. Geneva, Switzerland, 2002.
36. T. Jehan. Creating Music by Listening. PhD thesis, Massachusetts Institute of Technology, Boston, Mass., USA, Sept. 2005.
37. K. Jensen. A causal rhythm grouping. In Computer Music Modeling and Retrieval, volume 3310 of Lecture Notes in Computer Science, pages 83-95. Springer Berlin/Heidelberg, 2004.
38. K. Jensen. Multiple scale music segmentation using rhythm, timbre, and harmony. EURASIP Journal on Advances in Signal Processing, 2007. Article ID 73205, 11 pages.
39. F. Lerdahl and R. Jackendoff. A Generative Theory of Tonal Music. The MIT Press, Cambridge, Mass., USA, 1999.
40. M. Levy, K. Noland, and M. Sandler. A comparison of timbral and harmonic music segmentation algorithms. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 1433-1436, Honolulu, Hawaii, USA, Apr. 2007.
41. M. Levy and M. Sandler. Structural segmentation of musical audio by constrained clustering. IEEE Transactions on Audio, Speech, and Language Processing, 16(2):318-326, Feb. 2008.
42. M. Levy, M. Sandler, and M. Casey. Extraction of high-level musical structure from audio data and its application to thumbnail generation. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 13-16, Toulouse, France, May 2006.
43. B. Logan and S. Chu. Music summarization using key phrases. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 749-752, Istanbul, Turkey, June 2000.
44. L. Lu, M. Wang, and H.-J. Zhang. Repeating pattern discovery and structure analysis from acoustic music data. In Proc. of Workshop on Multimedia Information Retrieval, pages 275-282, New York, N.Y., USA, Oct. 2004.
45. H. Lukashevich. Towards quantitative measures of evaluating song segmentation. In Proc. of 9th International Conference on Music Information Retrieval, pages 375-380, Philadelphia, Pa., USA, Sept. 2008.
46. N. C. Maddage. Automatic structure detection for popular music. IEEE Multimedia, 13(1):65-77, Jan. 2006.
47. N. C. Maddage, C. Xu, M. S. Kankanhalli, and X. Shao. Contentbased music structure analysis with applications to music semantics understanding. In Proc. of ACM Multimedia, pages 112-119, New York, N.Y., USA, Oct. 2004.
48. M. Marolt. A mid-level melody-based representation for calculating audio similarity. In Proc. of 7th International Conference on Music Information Retrieval, pages 280-285, Victoria, B.C., Canada, Oct. 2006.
49. M. Mauch, C. Cannam, M. Davies, S. Dixon, C. Harte, S. Kolozali, D. Tidhar, and M. Sandler. OMRAS2 metadata project 2009. In Proc. of 10th International Conference on Music Information Retrieval, Kobe, Japan, Oct. 2009. Extended abstract.
50. M. Mauch, K. Noland, and S. Dixon. Using musical structure to enhance automatic chord transcription. In Proc. of 10th International Conference on Music Information Retrieval, pages 231-236, Kobe, Japan, Oct. 2009.
51. R. Middleton. Form. In B. Horner and T. Swiss, editors, Key terms in popular music and culture, pages 141-155. Wiley-Blackwell, Sept. 1999.
52. M. Müller. Information Retrieval for Music and Motion. Springer, 2007.
53. M. Müller and M. Clausen. Transposition-invariant self-similarity matrices. In Proc. of 8th International Conference on Music Information Retrieval, pages 47-50, Vienna, Austria, Sept. 2007.
54. M. Müller, S. Ewert, and S. Kreuzer. Making chroma features more robust to timbre changes. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 1877-1880, Taipei, Taiwan, Apr. 2009.
55. M. Müller and F. Kurth. Enhancing similarity matrices for music audio analysis. In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 437-440, Toulouse, France, May 2006.
56. M. Müller and F. Kurth. Towards structural analysis of audio recordings in the presence of musical variations. EURASIP Journal on Advances in Signal Processing, 2007.
57. B. S. Ong. Structural analysis and segmentation of musical signals. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain, 2006.
58. B. S. Ong, E. Gómez, and S. Streich. Automatic extraction of musical structure using pitch class distribution features. In Proc. of 1st Workshop on Learning the Semantics of Audio Signals, Athens, Greece, Dec. 2006.
59. J. Paulus and A. Klapuri.Music structure analysis by finding repeated parts. In Proc. of 1st ACM Audio and Music Computing Multimedia Workshop, pages 59-68, Santa Barbara, Calif., USA, Oct. 2006.
60. J. Paulus and A. Klapuri. Acoustic features for music piece structure analysis. In Proc. of 11th International Conference on Digital Audio Effects, pages 309-312, Espoo, Finland, Sept. 2008.
61. J. Paulus and A. Klapuri. Labelling the structural parts of a music piece with Markov models. In S. Ystad, R. Kronland-Martinet, and K. Jensen, editors, Computer Music Modeling and Retrieval: Genesis of Meaning in Sound and Music - 5th International Symposium, CMMR 2008 Copenhagen, Denmark, May 19-23, 2008, Revised Papers, volume 5493 of Lecture Notes in Computer Science, pages 166- 176. Springer Berlin/Heidelberg, 2009.
62. J. Paulus and A. Klapuri. Music structure analysis using a probabilistic fitness measure and a greedy search algorithm. IEEE Transactions on Audio, Speech, and Language Processing, 17(6):1159-1170, Aug. 2009.
63. G. Peeters. Deriving musical structure from signal analysis for music audio summary generation: "sequence" and "state" approach. In Computer Music Modeling and Retrieval, volume 2771 of Lecture Notes in Computer Science, pages 143-166. Springer Berlin/Heidelberg, 2004.
64. G. Peeters. Sequence representation of music structure using higherorder similarity matrix and maximum-likelihood approach. In Proc. of 8th International Conference on Music Information Retrieval, pages 35-40, Vienna, Austria, Sept. 2007.
65. G. Peeters. Template-based estimation of time-varying tempo. EURASIP Journal on Advances in Signal Processing, 2007(1):158- 158, 2007.
66. G. Peeters. MIREX 2009 "Music structure segmentation" task: Ircamsummary submission. In Proc. of Fifth Annual Music Information Retrieval Evaluation eXchange, Kobe, Japan, Oct. 2009. Extended abstract.
67. G. Peeters and E. Deruty. Is music structure annotation multidimensional? A proposal for robust local music annotation. In Proc. of 3rd Workshop on Learning the Semantics of Audio Signals, pages 75-90, Graz, Austria, Dec. 2009.
68. G. Peeters, A. La Burthe, and X. Rodet. Toward automatic music audio summary generation from signal analysis. In Proc. of 3rd International Conference on Music Information Retrieval, pages 94-100, Paris, France, Oct. 2002.
69. E. Peiszer. Automatic audio segmentation: Segment boundary and structure detection in popular music. Master's thesis, Vienna University of Technology, Vienna, Austria, Aug. 2007.
70. C. Rhodes and M. Casey. Algorithms for determining and labelling approximate hierarchical self-similarity. In Proc. of 8th International Conference on Music Information Retrieval, pages 41-46, Vienna, Austria, Sept. 2007.
71. M. P. Ryynänen and A. P. Klapuri. Automatic transcription ofmelody, bass line, and chords in polyphonic music. Computer Music Journal, 32(3):72-86, 2008.
72. Y. Shiu, H. Jeong, and C.-C. J. Kuo. Musical structure analysis using similarity matrix and dynamic programming. In Proc. of SPIE Vol. 6015 - Multimedia Systems and Applications VIII, pages 398-409, 2005.
73. Y. Shiu, H. Jeong, and C.-C. J. Kuo. Similar segment detection for music structure analysis via Viterbi algorithm. In Proc. of IEEE International Conference on Multimedia and Expo, pages 789-792, Toronto, Ont., Canada, July 2006.
74. H. Terasawa, M. Slaney, and J. Berger. The thirteen colors of timbre. In Proc. of 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pages 323-326, New Platz, N.Y., USA, Oct. 2005.
75. D. Turnbull, G. Lanckriet, E. Pampalk, and M. Goto. A supervised approach for detecting boundaries in music using difference features and boosting. In Proc. of 8th International Conference on Music Information Retrieval, pages 51-54, Vienna, Austria, Sept. 2007.
76. G. Tzanetakis and P. Cook. Multifeature audio segmentation for browsing and annotation. In Proc. of 1999 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pages 103-106, New Platz, N.Y., USA, Oct. 1999.
77. Y. Weiss. Segmentation using eigenvectors: a unifying view. In Proc. of Seventh IEEE International Conference on Computer Vision, pages 975-982, Kerkyra, Greece, Sept. 1999.
78. J. Wellhausen and M. Ḧoynck. Audio thumbnailing using MPEG-7 low-level audio descriptors. In Proc. of The SPIE Internet Multimedia Management Systems IV, volume 5242, pages 65-73, Nov. 2003.
79. C. Xu, X. Shao, N. C. Maddage, M. S. Kankanhalli, and T. Qi. Automatically summarize musical audio using adaptive clustering. In Proc. of IEEE International Conference on Multimedia and Expo, pages 2063-2066, Taipei, Taiwan, June 2004.
80. R. Zhou, M. Mattavelli, and G. Zoia. Music onset detection based on resonator time frequency image. IEEE Transactions on Audio, Speech, and Language Processing, 16(8):1685-1695, 2008.