MTSSM - A Framework for Multi-Track Segmentation of Symbolic Music
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:62150", author = "Brigitte Rafael and Stefan M. Oertl", title = "MTSSM - A Framework for Multi-Track Segmentation of Symbolic Music", 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.", volume = "4", number = "1", pages = "142-7", }
