Music Plagiarism Detection Based on Siamese CNN

Abstract

As music plagiarism has increased, various studies have been conducted on plagiarism detection. Conventional text-based plagiarism detection techniques identify plagiarism by comparing the similarity of musical information such as rhythms and notes. However, detecting plagiarized music that has subtle differences from the original music is still challenging. We propose a music plagiarism detection scheme (MPD-S) based on a Siamese convolutional neural network (CNN), which determines the presence or absence of plagiarism even with small changes in melody using Musical Instrument Digital Interface (MIDI) data. MPD-S converts vectorized MIDI data into grayscale images and then trains a CNN-based Siamese network model to measure the similarity between the original music and plagiarized music. MPD-S detects not only transposition and note plagiarism for a single vocal melody, but also fine melody plagiarism such as swapping and shift. MPD-S achieved a plagiarism detection accuracy of 98.7% for MIDI data, which is approximately 22.67% higher than that of the conventional plagiarism detection model.