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An approach to melodic segmentation and classification based on filtering with the Haar wavelet

Velarde, G., Weyde, T. & Meredith, D. (2013). An approach to melodic segmentation and classification based on filtering with the Haar wavelet. Journal of New Music Research, 42(4), pp. 325-345. doi: 10.1080/09298215.2013.841713


We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The
segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the
method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features.

Publication Type: Article
Additional Information: This is an Author's Original Manuscript of an article published by Taylor & Francis Group in Journal of New Music Research on 16 Dec 2013, available online:
Subjects: M Music and Books on Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Communication & Creativity > Performing Arts > Music
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