Plagiarism detection in polyphonic music using monaural signal separation
| Autor: | Bhiksha Raj, Sourish Chaudhuri, Kriti Suneja, Indradyumna Roy, Tarunima Prabhakar, Soham De, Rita Singh |
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| Rok vydání: | 2012 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Computer Science - Artificial Intelligence Computer science Speech recognition Feature vector SIGNAL (programming language) Monaural Computer Science - Sound Multimedia (cs.MM) Artificial Intelligence (cs.AI) Feature (computer vision) Similarity (psychology) Polyphony Plagiarism detection Representation (mathematics) Computer Science - Multimedia |
| Zdroj: | INTERSPEECH |
| DOI: | 10.21437/interspeech.2012-476 |
| Popis: | 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. Comment: Preprint version |
| Databáze: | OpenAIRE |
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