Abstract
This paper presents a comparison of the effectiveness of two computational intelligence approaches applied to the task of retrieving rhythmic structure from musical files. The method proposed by the authors of this paper generates rhythmic levels first, and then uses these levels to compose rhythmic hypotheses. Three phases: creating periods, creating simplified hypotheses and creating full hypotheses are examined within this study. All experiments are conducted on a database of national anthems. Decision systems such as Artificial Neural Networks and Rough Sets are employed to search the metric structure of musical files. This was based on examining physical attributes of sound that are important in determining the placement of a particular sound in the accented location of a musical piece. The results of the experiments show that both decision systems award note duration as the most significant parameter in automatic searching for metric structure of rhythm from musical files. Also, a brief description of the application realizing automatic rhythm accompaniment is presented.
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Kostek, B., Wójcik, J., Szczuko, P. (2008). Automatic Rhythm Retrieval from Musical Files. In: Peters, J.F., Skowron, A., Rybiński, H. (eds) Transactions on Rough Sets IX. Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89876-4_4
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