Abstract
In this paper, an effective method to discover repeating pattern from audio is proposed. Since the previous feature extraction methods are usually process monophony audio, for extracting more descriptive features from polyphony audio, Gabor filters bank is introduced. Meanwhile the measure criteria is suggested for qualitatively and quantitatively weighting the discernibility of extracted features. In addition, the presented algorithm is based on the incremental match and has time complexity O(nlog(n)). Experimental evaluations show that our proposed method could extract complete and meaningful repeating patterns from polyphony audio.
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© 2006 Springer-Verlag Berlin Heidelberg
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Du, ZL., Li, XL. (2006). Repeating Pattern Discovery from Audio Stream. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_26
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DOI: https://doi.org/10.1007/11739685_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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