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

2013-12-01

AI Summary

This work addresses automatic segmentation and classification of symbolic melodies, specifically targeting two tasks: attribution of excerpts from Bach’s Two-Part Inventions (BWV 772–786) to their parent works, and assignment of 360 Dutch folk songs to one of 26 tune families. We propose a single-scale continuous Haar wavelet filtering method that models pitch sequences as time-series signals; segmentation is driven by detection of local extrema and zero crossings, followed by k-nearest neighbors classification using Euclidean or Manhattan distance. This is the first application of Haar wavelets directly for melodic structural representation and segmentation—bypassing heuristic Gestalt-based rules and enabling cross-modal integration of signal processing and music cognition. Experiments show our method significantly outperforms both unfiltered pitch sequences and Gestalt-based segmentation on Bach excerpt attribution, and achieves performance close to the pitch-based baseline on folk tune family classification—slightly below state-of-the-art multi-feature string-matching approaches.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Abstract 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. This 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.

Problem

• Segmenting melodies using Haar-wavelet filtering
• Classifying melody segments with k-nearest neighbor
• Comparing effectiveness with unfiltered pitch signals

Innovation

• Haar-wavelet filtering for melody segmentation
• k-nearest neighbour algorithm for segment classification
• Local maxima or zero-crossings for segment detection