Plagiarism Detection in Polyphonic Music using Monaural Signal Separation

TL;DR

This paper introduces a novel feature space derived from signal separation techniques to improve automated plagiarism detection in polyphonic music, addressing limitations of traditional similarity measures.

Contribution

It presents a new feature representation that accounts for polyphony in music, enhancing plagiarism detection accuracy without high computational costs.

Findings

Significant improvement over standard baselines in detecting music plagiarism.

Effective combination of compositional modeling features with traditional audio features.

Ensemble classification framework enhances detection performance.

Abstract

Given the large number of new musical tracks released each year, automated approaches to plagiarism detection are essential to help us track potential violations of copyright. Most current approaches to plagiarism detection are based on musical similarity measures, which typically ignore the issue of polyphony in music. We present a novel feature space for audio derived from compositional modelling techniques, commonly used in signal separation, that provides a mechanism to account for polyphony without incurring an inordinate amount of computational overhead. We employ this feature representation in conjunction with traditional audio feature representations in a classification framework which uses an ensemble of distance features to characterize pairs of songs as being plagiarized or not. Our experiments on a database of about 3000 musical track pairs show that the new feature space characterization produces significant improvements over standard baselines.