Audio cover song identification and similarity: Background, approaches, evaluation, and beyond

Studies in Computational Intelligence

Volumn 274, Issue , 2010, Pages 307-332

  Serrà, Joan ^a   Gómez, Emilia ^a   Herrera, Perfecto ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77949610162     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-11674-2_14     Document Type: Article

References (91)

1. Adams, N.H., Bartsch, N.A., Shifrin, J.B., Wakefield, G.H.: Time series alignment for music information retrieval. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 303-310 (2004)
2. Ahonen, T.E., Lemstrom, K.: Identifying cover songs using normalized compression distance. In: Int. Workshop on Machine Learning and Music, MML (July 2008)
3. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press Books, New York (1999)
4. Bello, J.P.: Audio-based cover song retrieval using approximate chord sequences: testing shifts, gaps, swaps and beats. In: Int. Symp. on Music Information Retrieval (ISMIR), September 2007, pp. 239-244 (2007)
5. Bello, J.P., Pickens, J.: A robust mid-level representation for harmonic content in music signals. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 304-311 (2005)
6. Berenzweig, A., Logan, B., Ellis, D.P.W., Whitman, B.: A large scale evaluation of acoustic and subjective music similarity measures. In: Int. Symp. on Music Information Retrieval, ISMIR (2003)
7. Cano, P., Batlle, E., Kalker, T., Haitsma, J.: A review of audio fingerprinting. Journal of VLSI Signal Processing 41, 271-284 (2005)
8. Casey, M., Rhodes, C., Slaney, M.: Analysis of minimum distances in high-dimensional musical spaces. IEEE Trans. on Audio, Speech, and Language Processing 16(5), 1015-1028 (2008)
9. Casey, M., Slaney, M.: The importance of sequences in musical similarity. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), May 2006, vol.5, p. V (2006)
10. Casey, M., Veltkamp, R.C., Goto, M., Leman,M., Rhodes, C., Slaney, M.: Content-based music information retrieval: current directions and future challenges. Proceedings of the IEEE 96(4), 668-696 (2008)
11. Dalla Bella, S., Peretz, I., Aronoff, N.: Time course of melody recognition: a gating paradigm study. Perception and Psychophysics 7(65), 1019-1028 (2003)
12. Dannenberg, R.B., Birmingham, W.P., Pardo, B., Hu, N., Meek, C., Tzanetakis, G.: A comparative evaluation of search techniques for query-by-humming using the musart testbed. Journal of the American Society for Information Science and Technology 58(5), 687-701 (2007)
13. de Cheveigńe, A.: Pitch perception models. In: Plack, C.J., Oxenham, A., Fay, R.R., Popper, A.N. (eds.) Pitch - Neural coding and perception, pp. 169-233. Springer, New York (2005)
14. Delìege, I.: Cue abstraction as a component of categorisation processes in music listening. Psychology of Music 24(2), 131-156 (1996)
15. Dixon, S., Widmer, G.: Match: A music alignment tool chest. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 492-497 (2005)
16. Dowling, W.J.: Scale and contour: two components of a theory of memory for melodies. Psychological Review 85(4), 341-354 (1978)
17. Dowling, W.J., Harwood, J.L.: Music cognition. Academic Press, London (1985)
18. Downie, J.S.: The music information retrieval evaluation exchange (2005-2007): a window into music information retrieval research. Acoustical Science and Technology 29(4), 247-255 (2008)
19. Downie, J.S., Bay, M., Ehmann, A.F., Jones, M.C.: Audio cover song identification: MIREX 2006-2007 results and analyses. In: Int. Symp. on Music Information Retrieval (ISMIR), September 2008, pp. 468-473 (2008)
20. Egorov, A., Linetsky, G.: Cover song identification with IF-F0 pitch class profiles. In: MIREX extended abstract (September 2008)
21. Ellis, D.P.W., Cotton, C.: The 2007 labrosa cover song detection system. In: MIREX extended abstract (September 2007)
22. Ellis, D.P.W., Cotton, C., Mandel, M.: Cross-correlation of beat-synchronous representations for music similarity. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), April 2008, pp. 57-60 (2008)
23. Ellis, D.P.W., Poliner, G.E.: Identifying cover songs with chroma features and dynamic programming beat tracking. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), April 2007, vol.4, pp. 1429-1432 (2007)
24. Ellis, D.P.W.,Whitman, B., Berenzweig, A., Lawrence, S.: The quest for ground truth in musical artist similarity. In: Int. Symp. onMusic Information Retrieval (ISMIR), October 2002, pp. 518-529 (2002)
25. Foote, J.: Arthur: Retrieving orchestral music by long-term structure. In: Int. Symp. on Music Information Retrieval (ISMIR) (October 2000)
26. Fujishima, T.: Realtime chord recognition of musical sound: a system using common lisp music. In: Int. Computer Music Conference (ICMC), pp. 464-467 (1999)
27. Ǵomez, E.: Tonal description of music audio signals. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain (2006), http://mtg.upf.edu/node/472
28. Ǵomez, E., Herrera, P.: The song remains the same: identifying versions of the same song using tonal descriptors. In: Int. Symp. on Music Information Retrieval (ISMIR), October 2006, pp. 180-185 (2006)
29. Ǵomez, E., Ong, B.S., Herrera, P.: Automatic tonal analysis from music summaries for version identification. In: Conv. of the Audio Engineering Society (AES) (October 2006); CD-ROM, paper no. 6902
30. Gouyon, F., Klapuri, A., Dixon, S., Alonso, M., Tzanetakis, G., Uhle, C., Cano, P.: An experimental comparison of audio tempo induction algorithms. IEEE Trans. on Speech and Audio Processing 14(5), 1832-1844 (2006)
31. Gusfield, D.: Algorithms on strings, trees and sequences: computer sciences and computational biology. Cambridge University Press, Cambridge (1997)
32. Harte, C.A., Sandler, M.B.: Automatic chord identification using a quantized chromagram. In: Conv. of the Audio Engineering Society (AES), pp. 28-31 (2005)
33. Heikkila, J.: A new class of shift-invariant operators. IEEE Signal Processing Magazine 11(6), 545-548 (2004)
34. Hu, N., Dannenberg, R.B., Tzanetakis, G.: Polyphonic audio matching and alignment for music retrieval. In: IEEE Workshop on Apps. of Signal Processing to Audio and Acoustics (WASPAA), pp. 185-188 (2003)
35. Izmirli, Ö.: Tonal similarity from audio using a template based attractor model. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 540-545 (2005)
36. Jensen, J.H., Christensen,M.G., Ellis,D.P.W., Jensen, S.H.:A tempo-insensitive distance measure for cover song identification based on chroma features. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), April 2008, pp. 2209-2212 (2008)
37. Jensen, J.H., Christensen, M.G., Jensen, S.H.: A chroma-based tempo-insensitive distance measure for cover song identification using the 2d autocorrelation. In: MIREX extended abstract (September 2008)
38. Kim, S., Narayanan, S.: Dynamic chroma feature vectors with applications to cover song identification. In: IEEE Workshop on Multimedia Signal Processing (MMSP), October 2008, pp. 984-987 (2008)
39. Kim, S., Unal, E., Narayanan, S.: Fingerprint extraction for classical music cover song identification. In: IEEE Int. Conf. on Multimedia and Expo (ICME), June 2008, pp. 1261-1264 (2008)
40. Kim, Y.E., Perelstein, D.: MIREX 2007: audio cover song detection using chroma features and hidden markov model. In: MIREX extended abstract (September 2007)
41. Kurth, F., M̈uller, M.: Efficient index-based audio matching. IEEE Trans. on Audio, Speech, and Language Processing 16(2), 382-395 (2008)
42. Larkin, C. (ed.): The Encyclopedia of Popular Music, 3rd edn. (November 1998)
43. Lee, K.: Identifying cover songs from audio using harmonic representation. In: MIREX extended abstract (September 2006)
44. Lee, K.: A system for acoustic chord transcription and key extraction from audio using hidden Markov models trained on synthesized audio. PhD thesis, Stanford University, USA (2008)
45. Lemstrom, K.: String matching techinques for music retrieval. PhD thesis, University of Helsinki, Finland (2000)
46. Levitin, D.: This is your brain on music: the science of a human obsession. Penguin (2007)
47. Manning, C.D., Prabhakar, R., Schutze, H.: An introduction to Information Retrieval. Cambridge University Press, Cambridge (2008), http://www. informationretrieval.org
48. Mardirossian, A., Chew, E.:Music summarization via key distributions: analyses of similarity assessment across variations. In: Int. Symp. on Music Information Retrieval, ISMIR (2006)
49. Marolt, M.: A mid-level melody-based representation for calculating audio similarity. In: Int. Symp. on Music Information Retrieval (ISMIR), October 2006, pp. 280-285 (2006)
50. Marolt, M.: A mid-level representation for melody-based retrieval in audio collections. IEEE Trans. on Multimedia 10(8), 1617-1625 (2008)
51. Miotto, R., Orio, N.: A music identification system based on chroma indexing and statistical modeling. In: Int. Symp. on Music Information Retrieval (ISMIR), September 2008, pp. 301-306 (2008)
52. M̈uller, M.: Information Retrieval for Music and Motion. Springer, Heidelberg (2007)
53. M̈uller, M., Kurth, F., Clausen, M.: Audio matching via chroma-based statistical features. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 288-295 (2005)
54. Myers, C.: A comparative study of several dynamic time warping algorithms for speech recognition. Master's thesis, Massachussets Institute of Technology, USA (1980)
55. Nagano, H., Kashino, K., Murase, H.: Fast music retrieval using polyphonic binary feature vectors. In: IEEE Int. Conf. on Multimedia and Expo (ICME), vol.1, pp. 101-104 (2002)
56. Navarro, G., Ḿakinen, V., Ukkonen, E.: Algorithms for transposition invariant string matching. Journal of Algorithms (56) (2005)
57. Ong, B.S.: Structural analysis and segmentation of music signals. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain (2007), http://mtg.upf.edu/ node/508
58. Oppenheim, A.V., Schafer, R.W., Buck, J.B.: Discrete-Time Signal Processing, 2nd edn. Prentice Hall, Englewood Cliffs (1999)
59. Pachet, F.: Knowledge management and musical metadata. Idea Group (2005)
60. Papadopoulos, H., Peeters, G.: Large-scale study of chord estimation algorithms based on chroma representation and hmm. In: Int. Conf. on Content-Based Multimedia Information, pp. 53-60 (2007)
61. Pickens, J.: Harmonic modeling for polyphonic music retrieval. PhD thesis, University of Massachussetts Amherst, USA (2004)
62. Poliner, G.E., Ellis, D.P.W., Ehmann, A., Ǵomez, E., Streich, S., Ong, B.S.:Melody transcription from music audio: approaches and evaluation. IEEE Trans. on Audio, Speech, and Language Processing 15, 1247-1256 (2007)
63. Purwins, H.: Proles of pitch classes. Circularity of relative pitch and key: experiments, models, computational music analysis, and perspectives. PhD thesis, Berlin University of Technology, Germany (2005)
64. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE (1989)
65. Rabiner, L.R., Juang, B.H.: Fundamentals of speech recognition. Prentice Hall, Englewood Cliffs (1993)
66. Riley, M., Heinen, E., Ghosh, J.: A text retrieval approach to content-based audio retrieval. In: Int. Symp. on Music Information Retrieval (ISMIR), September 2008, pp. 295-300 (2008)
67. Robine, M., Hanna, P., Ferraro, P., Allali, J.: Adaptation of string matching algorithms for identification of near-duplicate music documents. In: ACM SIGIRWorkshop on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection (PAN), pp. 37-43 (2007)
68. Sailer, C., Dressler, K.: Finding cover songs by melodic similarity. In: MIREX extended abstract (September 2006)
69. Sankoff, D., Kruskal, J.: Time warps, string edits, and macromolecules. Addison-Wesley, Reading (1983)
70. Scaringella, N., Zoia, G., Mlynek, D.: Automatic genre classification of music content: a survey. IEEE Signal Processing Magazine 23(2), 133-141 (2006)
71. Schellenberg, E.G., Iverson, P., McKinnon, M.C.: Name that tune: identifying familiar recordings from brief excerpts. Psychonomic Bulletin and Review 6(4), 641-646 (1999)
72. Schulkind, M.D., Posner, R.J., Rubin, D.C.:Musical features that facilitate melody identification: how do you know it's your song when they finally play it? Music Perception 21(2), 217-249 (2003)
73. Selfridge-Field, E.: Conceptual and representational issues in melodic comparison. MIT Press, Cambridge (1998)
74. Serr̀a, J., Serra, X., Andrzejak, R.G.: Cross recurrence quantification for cover song identification. New Journal of Physics 11, art. 093017 (September 2009)
75. Serr̀a, J., Ǵomez, E., Herrera, P.: Transposing chroma representations to a common key. In: IEEE CS Conference on The Use of Symbols to Represent Music and Multimedia Objects, October 2008, pp. 45-48 (2008)
76. Serr̀a, J., Ǵomez, E., Herrera, P., Serra, X.: Chroma binary similarity and local alignment applied to cover song identification. IEEE Trans. on Audio, Speech, and Language Processing 16(6), 1138-1152 (2008)
77. Sheh, A., Ellis, D.P.W.: Chord segmentation and recognition using em-trained hidden markov models. Int. Symp. on Music Information Retrieval (ISMIR), pp. 183-189 (2003)
78. Tsai, W.H., Yu, H.M., Wang, H.M.: A query-by-example technique for retrieving cover versions of popular songs with similar melodies. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 183-190 (2005)
79. Tsai, W.H., Yu, H.M., Wang, H.M.: Using the similarity of main melodies to identify cover versions of popular songs for music document retrieval. Journal of Information Science and Engineering 24(6), 1669-1687 (2008)
80. Tversky, A.: Features of similarity. Psychological Review 84, 327-352 (1977)
81. Typke, R.: Music retrieval based on melodic similarity. PhD thesis, Utrecht University, Netherlands (2007)
82. Tzanetakis, G.: Pitch histograms in audio and symbolic music information retrieval. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 31-38 (2002)
83. Ukkonen, E., Lemstrom, K., M ̈akinen, V.: Sweepline the music! Comp. Sci. in Perspective, 330-342 (2003)
84. Unal, E., Chew, E.: Statistical modeling and retrieval of polyphonic music. In: IEEE Workshop on Multimedia Signal Processing (MMSP), pp. 405-409 (2007)
85. Vignoli, F., Paws, S.: A music retrieval system based on user-driven similarity and its evaluation. In: Int. Symp. on Music Information Retrieval (ISMIR), pp. 272-279 (2005)
86. Voorhees, E.M., Harman, D.K.: Trec: Experiment and evaluation in information retrieval (2005)
87. White, B.W.: Recognition of distorted melodies. American Journal of Psychology 73, 100-107 (1960)
88. Xu, R.,Wunsch, D.C.: Clustering. IEEE Press, Los Alamitos (2009)
89. Yang, C.: Music database retrieval based on spectral similarity. Technical report (2001)
90. Yu, Y., Downie, J.S., Chen, L., Oria, V., Joe, K.: Searching musical audio datasets by a batch of multi-variant tracks. In: ACM Multimedia, October 2008, pp. 121-127 (2008)
91. Yu, Y., Downie, J.S., M̈orchen, F., Chen, L., Joe, K., Oria, V.: Cosin: content-based retrieval system for cover songs. In: ACM Multimedia, October 2008, pp. 987-988 (2008)