MTSSM - A Framework for Multi-Track Segmentation of Symbolic Music

Authors: Brigitte Rafael, Stefan M. Oertl

Abstract:

Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the internal structure of a composition. Structural information about a composition can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. The authors of this paper present the MTSSM framework, a twolayer framework for the multi-track segmentation of symbolic music. The strength of this framework lies in the combination of existing methods for local track segmentation and the application of global structure information spanning via multiple tracks. The first layer of the MTSSM uses various string matching techniques to detect the best candidate segmentations for each track of a multi-track composition independently. The second layer combines all single track results and determines the best segmentation for each track in respect to the global structure of the composition.

Keywords: Pattern Recognition, Music Information Retrieval, Machine Learning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080540

References:

[1] M. Levy, K. Noland, and M. Sandler, "A comparison of timbral and harmonic music segmentation algorithms," in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 4, 2007, pp. 1433-1436.
[2] K. Jensen, "Multiple scale music segmentation using rhythm, timbre, and harmony," EURASIP Journal on Applied Signal Processing, vol. 2007, no. 1, 2007.
[3] S. Downie and M. Nelson, "Evaluation of a simple and effective music information retrieval method," in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR),, 2000, pp. 73-80.
[4] E. Isaacson, "What you see is what you get: On visualizing music," in Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2005, pp. 389-395.
[5] M. Cooper and J. Foote, "Summarizing popular music via structural similarity analysis," in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2003, pp. 127-130.
[6] K. Lee and M. Cremer, "Segmentation-based lyrics-audio alignment using dynamic programming," in Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR), 2008, pp. 395-400.
[7] E. Peiszer, "Automatic audio segmentation: Segment boundary and structure detection in popular music," Master-s thesis, Vienna University of Technology, Vienna, Austria, 2007.
[8] J. Paulus and A. Klapuri, "Music structure analysis by finding repeated parts," in Proceedings of the 1st ACM Workshop on Audio and Music Computing for Multimedia (AMCMM). ACM, 2006, pp. 59-68.
[9] M. Mueller and S. Ewert, "Joint structure analysis with applications to music annotation and synchronization," in Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR), 2008, pp. 389-394.
[10] S. Perttu, "Combinatorial pattern matching in musical sequences," 2000.
[11] M. Crochmore, C. S. Iliopoulos, T. Lecroq, and Y. J. Pinzon, "Approximate string matching in musical sequences," in Proceedings Prague Stringology Club (PSC), 2001, pp. 26-36.
[12] T. Crawford, "String-matching techniques for musical similarity and melodic recognition," Computing in Musicology, pp. 73-100, 1998.
[13] V. Mkinen, G. Navarro, and E. Ukkonen, "Transposition invariant string matching," Journal of Algorithms, vol. 56, pp. 124-153, 2005.
[14] C. Charras and T. Lecrog, Handbook of Exact String Matching Algorithms. King-s College Publications, 2004.
[15] M. Crochemore, "An optimal algorithm for computing the repetitions in a word," Information Processing Letters, vol. 12, pp. 244-250, 1981.
[16] C. S. Iliopoulo, D. W. G. Moore, and K. Park, "Covering a string," Algorithmica, vol. 16, pp. 288-297, 1996.
[17] E. Cambouropoulos, "Musical parallelism and melodic segmentation: A computational approach," Music Perception, vol. 23, no. 3, pp. 249-269, 2006.
[18] E. Cambouropulos, M. Crochemore, C. S. Iliopoulos, L. Mouchard, and Y. J. Pinzon, "Algorithms for computing approximate repetitions in musical sequences," International Journal of Computer Mathematics, vol. 79, no. 11, pp. 1135-1148, 2002.
[19] J. Hsu, C. Liu, and A. Chen, "Discovering nontrivial repeating patterns in music data," in IEEE Transactions on Multimedia, vol. 3, 2001, pp. 311-325.
[20] T. Jehan, "Hierarchical multi-class self similarities," in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2005, pp. 311-314.