Plagiarism Detection in Polyphonic Music using Monaural Signal Separation
This addresses copyright enforcement for music creators and distributors by improving automated detection of plagiarism in complex polyphonic tracks, though it is incremental as it builds on existing classification frameworks.
The paper tackled plagiarism detection in polyphonic music by developing a novel feature space derived from signal separation techniques to account for polyphony without high computational cost, and experiments on 3000 track pairs showed significant improvements over standard baselines.
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.