Abstract
The intuitive mode of structuring melodies by humans is very hard to reproduce in the context of an automated method. The human brain can differentiate between the pitch, timbre and the attack of a musical note even if the listener doesn’t have prior knowledge of musical theory; the successions of these notes could easily be the base for recognizing various sections of a song. This paper tries to give some insight in the problem of automatic structuring of musical content, by applying some techniques of machine learning. The experiment followed a TOP-DOWN approach by applying the algorithms on 7 different genres, afterwards on one album of a particular genre and in the end on a single audio file of the same genre. After the automatic structure analysis was performed the accuracy of the results was tested by a performance evaluator and by a human component.
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© 2012 Springer-Verlag Berlin Heidelberg
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Simion, A., Trăușan-Matu, Ș. (2012). Towards Automatic Structure Analysis of Digital Musical Content. In: Ramsay, A., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2012. Lecture Notes in Computer Science(), vol 7557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33185-5_25
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DOI: https://doi.org/10.1007/978-3-642-33185-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33184-8
Online ISBN: 978-3-642-33185-5
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