Automatic Reduction of MIDI Files Preserving Relevant Musical Content
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.
KeywordsGround Truth Music Information Retrieval Pitch Class Skyline Algorithm Midi File
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- 1.Chai, W.: Melody retrieval on the web. Master’s thesis, MIT (2001)Google Scholar
- 4.de Nooijer, J.: Cognition-based Segmentation for Music Information Retrieval Systems. Master’s thesis, Utrecht University (2007)Google Scholar
- 6.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)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 10.Meyer, L.B.: Emotion and Meaning in Music. The University of Chicago Press, Chicago (1956)Google Scholar
- 12.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)Google Scholar
- 13.Snyder, B.: Music and Memory: An Introduction. MIT Press, Cambridge (2000)Google Scholar
- 14.Typke, R.: Music Retrieval based on Melodic Similarity. PhD thesis, Department of Information and Computing Sciences, Universiteit Utrecht, Netherlands (2007)Google Scholar
- 15.Uitdenbogerd, A.L., Zobel, J.: Manipulation of music for melody matching. In: ACM Multimedia, pp. 235–240 (1998)Google Scholar