Abstract
Retrieval and classification are at the center of Music Information Retrieval research. Both tasks rely on a method to assess the similarity between two music documents. In the context of symbolically encoded melodies, pairwise alignment via dynamic programming has been the most widely used method. However, this approach fails to scale-up well in terms of time complexity and insufficiently models the variance between melodies of the same class. Compact representations and indexing techniques that capture the salient and robust properties of music content, are increasingly important. We adapt two existing bioinformatics tools to improve the melody retrieval and classification tasks. On two datasets of folk tunes and cover song melodies, we apply the extremely fast indexing method of the Basic Local Alignment Search Tool (BLAST) and achieve comparable classification performance to exhaustive approaches. We increase retrieval performance and efficiency by using multiple sequence alignment algorithms for locating variation patterns and profile hidden Markov models for incorporating those patterns into a similarity model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)
Bairoch, A.: Prosite: a dictionary of sites and patterns in proteins. Nucleic Acids Res. 19(Suppl), 2241 (1991)
Bertin-Mahieux, T., Ellis, D.P.W.: Large-scale cover song recognition using hashed chroma landmarks. In: Applications of Signal Processing to Audio and Acoustics, pp. 117–120 (2011)
Boot, P., Volk, A., de Haas, W.B.: Evaluating the role of repeated patterns in folk song classification and compression. J. New Music Res. 1–16 (2016)
Bountouridis, D., Koops, H.V., Wiering, F., Veltkamp, R.C.: Music outlier detection using multiple sequence alignment and independent ensembles. In: Amsaleg, L., Houle, M.E., Schubert, E. (eds.) SISAP 2016. LNCS, vol. 9939, pp. 286–300. Springer, Cham (2016). doi:10.1007/978-3-319-46759-7_22
Bountouridis, D., Van Balen, J.: The cover song variation dataset. In: The International Workshop on Folk Music Analysis (2014)
Carroll, H., Clement, M.J., Ridge, P., Snell, Q.O.: Effects of gap open and gap extension penalties. In: The Biotechnology and Bioinformatics Symposium, pp. 19–23 (2006)
Casey, M., Slaney, M.: Fast recognition of remixed music audio. In: Acoustics, Speech and Signal Processing, vol. 4, p. IV-1425 (2007)
Chai, W., Vercoe, B.: Folk music classification using hidden Markov models. In: International Conference on Artificial Intelligence, number 6 in 4. Citeseer (2001)
Day, W.H.E., McMorris, F.R.: Threshold consensus methods for molecular sequences. J. Theor. Biol. 159(4), 481–489 (1992)
Dong, X.L., Berti-Equille, L., Srivastava, D.: Integrating conflicting data: the role of source dependence. Proc. VLDB Endowment 2(1), 550–561 (2009)
Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)
Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14(9), 755–763 (1998)
Finn, R.D., Clements, J., Eddy, S.R.: Hmmer web server: interactive sequence similarity searching. Nucleic Acids Res. gkr367 (2011)
Gómez, E., Klapuri, A., Meudic, B.: Melody description and extraction in the context of music content processing. J. New Music Res. 32(1), 23–40 (2003)
Hillewaere, R., Manderick, B., Conklin, D.: Alignment methods for folk tune classification. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds.) Data Analysis, Machine Learning and Knowledge Discovery, pp. 369–377. Springer, Cham (2014)
Hogeweg, P., Hesper, B.: The alignment of sets of sequences and the construction of phyletic trees: an integrated method. J. Mol. Evol. 20(2), 175–186 (1984)
Hu, N., Dannenberg, R.B., Tzanetakis, G.: Polyphonic audio matching and alignment for music retrieval. Computer Science Department, p. 521 (2003)
Katoh, K., Misawa, K., Kuma, K., Miyata, T.: Mafft: a novel method for rapid multiple sequence alignment based on fast fourier transform. Nucleic Acids Res. 30(14), 3059–3066 (2002)
Kemena, C., Notredame, C.: Upcoming challenges for multiple sequence alignment methods in the high-throughput era. Bioinformatics 25(19), 2455–2465 (2009)
Kilian, J., Hoos, H.H.: Musicblast-gapped sequence alignment for MIR. In: International Society for Music Information Retrieval Conference, pp. 38–41 (2004)
Kim, S., Narayanan, S.: Dynamic chroma feature vectors with applications to cover song identification. In: Multimedia Signal Processing, pp. 984–987 (2008)
Koops, H.V., de Haas, W.B., Bountouridis, D., Volk, A.: Integration and quality assessment of heterogeneous chord sequences using data fusion. In: International Society for Music Information Retrieval Conference, pp. 178–184 (2016)
Krogh, A.: An introduction to hidden Markov models for biological sequences. New Compr. Biochem. 32, 45–63 (1998)
Krogh, A., Brown, M., Saira Mian, I., Sjölander, K., Haussler, D.: Hidden Markov models in computational biology: applications to protein modeling. J. Mol. Biol. 235(5), 1501–1531 (1994)
Malt, B.C.: An on-line investigation of prototype and exemplar strategies in classification. J. Exp. Psychol. Learn. Mem. Cogn. 15(4), 539 (1989)
Margulis, E.H.: Musical repetition detection across multiple exposures. Music Percept. Interdisc. J. 29(4), 377–385 (2012)
Martin, B., Brown, D.G., Hanna, P., Ferraro, P.: Blast for audio sequences alignment: a fast scalable cover identification. In: International Society for Music Information Retrieval Conference, pp. 529–534 (2012)
Müller, M., Mattes, H., Kurth, F.: An efficient multiscale approach to audio synchronization. In: International Society for Music Information Retrieval Conference, pp. 192–197. Citeseer (2006)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Pampalk, E.: Computational models of music similarity and their application in music information retrieval. na (2006)
Ratanamahatana, C.A., Keogh, E.: Everything you know about dynamic time warping is wrong. In: Third Workshop on Mining Temporal and Sequential Data, pp. 1–11. Citeseer (2004)
Serra, J., Gómez, E., Herrera, P., Serra, X.: Chroma binary similarity and local alignment applied to cover song identification. IEEE Trans. Audio Speech Lang. Process. 16(6), 1138–1151 (2008)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Temperley, D.: Bayesian models of musical structure and cognition. Musicae Sci. 8(2), 175–205 (2004)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994)
van Kranenburg, P.: A computational approach to content-based retrieval of folk song melodies. Ph.D. thesis, Utrecht University (2010)
van Kranenburg, P., de Bruin, M., Grijp, L., Wiering, F.: The Meertens tune collections. In: Meertens Online Reports (2014)
Volk, A., Haas, W.B., Van Kranenburg, P.: Towards modelling variation in music as foundation for similarity. In: Proceedings of the 12th International Conference on Music Perception and Cognition (2012)
Volk, A., Van Kranenburg, P.: Melodic similarity among folk songs: an annotation study on similarity-based categorization in music. Musicae Sci. 16, 317–339 (2012). page 1029864912448329
Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1(4), 337–348 (1994)
Wang, S., Ewert, S., Dixon, S.: Robust joint alignment of multiple versions of a piece of music. In: International Society for Music Information Retrieval, pp. 83–88 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bountouridis, D., Brown, D., Koops, H.V., Wiering, F., Veltkamp, R.C. (2017). Melody Retrieval and Classification Using Biologically-Inspired Techniques. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-55750-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55749-6
Online ISBN: 978-3-319-55750-2
eBook Packages: Computer ScienceComputer Science (R0)