Abstract
Retrieving music from large digital databases is a demanding computational task. The cost of indexing and searching depends on the computational effort of measuring musical similarity, but also heavily on the number and sizes of files in the database. One way to speed up music retrieval is to reduce the search space by removing redundant and uninteresting material in the database. We propose a simple measure of ‘interestingness’ based on music complexity, and present a reduction algorithm for MIDI files based on this measure. It is evaluated by comparing reduction ratios and the correctness of retrieval results for a query by humming task before and after applying the reduction.
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
Preview
Unable to display preview. Download preview PDF.
References
Chai, W.: Melody retrieval on the web. Master’s thesis, MIT (2001)
Cilibrasi, R., Vitányi, P., de Wolf, R.: Algorithmic clustering of music based on string compression. Computer Music Journal 28(4), 49–67 (2004)
Conklin, D.: Melodic analysis with segment classes. Machine Learning 65(2-3), 349–360 (2006)
de Nooijer, J.: Cognition-based Segmentation for Music Information Retrieval Systems. Master’s thesis, Utrecht University (2007)
Dubnov, S., McAdams, S., Reynolds, R.: Structural and affective aspects of music from statistical audio signal analysis. Journal of the American Society for Information Science and Technology 57(11), 1526–1536 (2006)
Li, M., Sleep, R.: Genre classification via an LZ78-based string kernel. In: Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, U.K. (2005)
Lui, S., Horner, A., Ayers, L.: MIDI to SP-MIDI transcoding using phrase stealing. IEEE Multimedia 13(2), 52–59 (2006)
Madsen, S.T., Widmer, G.: A complexity-based approach to melody track identification in midi files. In: Proceedings of the International Workshop on Artificial Intelligence and Music (MUSIC-AI 2007) held at the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India (January 2007)
Madsen, S.T., Widmer, G.: Towards a computational model of melody identification in polyphonic music. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India (January 2007)
Meyer, L.B.: Emotion and Meaning in Music. The University of Chicago Press, Chicago (1956)
Orr, M.G., Ohlsson, S.: Relationship Between Complexity and Liking as a Function of Expertise. Music Perception 22(4), 583–611 (2005)
Ruppin, A., Yeshurun, H.: MIDI Music Genre Classification by Invariant Features. In: Proceedings of the 7th International Conference on Music Information Retrieval, Victoria, BC, Canada, October 2006, pp. 397–399 (2006)
Snyder, B.: Music and Memory: An Introduction. MIT Press, Cambridge (2000)
Typke, R.: Music Retrieval based on Melodic Similarity. PhD thesis, Department of Information and Computing Sciences, Universiteit Utrecht, Netherlands (2007)
Uitdenbogerd, A.L., Zobel, J.: Manipulation of music for melody matching. In: ACM Multimedia, pp. 235–240 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tjagvad Madsen, S., Typke, R., Widmer, G. (2010). Automatic Reduction of MIDI Files Preserving Relevant Musical Content. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds) Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music. AMR 2008. Lecture Notes in Computer Science, vol 5811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14758-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-14758-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14757-9
Online ISBN: 978-3-642-14758-6
eBook Packages: Computer ScienceComputer Science (R0)