Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification

• URL: http://arxiv.org/abs/2504.20522v1
• Date: Tue, 29 Apr 2025 08:02:37 GMT
• Title: Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification
• Authors: Gissel Velarde, Tillman Weyde, David Meredith,
• Abstract summary: The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering.<n>We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales.<n>We found that the wavelet based segmentation and wavelet-filtering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized.
• Score: 2.4774640776820105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestalt-based method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients' local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and wavelet-filtering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized.

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