Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification
This work addresses the domain-specific problem of folk music analysis for researchers or archivists, presenting an incremental improvement over existing methods.
The study tackled the problem of segmenting and classifying symbolic folk music into tune families by applying Haar-wavelet filtering to pitch-time signals, resulting in improved classification accuracy compared to a Gestalt-based method when parameters were optimized.
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.