NORMA eResearch @NCI Library

Identification and Detection of Plagiarism in Music using Machine Learning Algorithms

Nair, Rajesh Ramachandran (2021) Identification and Detection of Plagiarism in Music using Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (1MB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (1MB) | Preview

Abstract

The occurrence of a substantial resemblance between two words is the closest description offered of plagiarism. It’s no surprise because song similarity is based on so many distinct elements and their combinations that it’s nearly difficult to combine them all into a single final rule of thumb. The combination of similarities between the rhythm and melody can be suspected of plagiarism. In this paper, we will do research based on the identification of plagiarism in music The dataset used here will be a simple set of MIDI files that have got only the melody track. First feature extraction has been performed here to extract the note or the chord progression then, the harmonic reduction is performed to understand the structure of the music and then using Word2Vec model is applied to get the relationship between similar chords to perform chord substitution which will be the final data that is extracted for the classifier models(KNN, Logistic Regression, Random Forest, Decision Tree, Gaussian Naive Bayes) to predict plagiarism and the results were obtained. After the training where quite interesting Naive Bayes performed poorly but among the 4 models, Random Forest performed with the highest accuracy of 98% after the model was trained for threshold value 0.5. These models have trained again with various other threshold values and the appropriate results were obtained.

Item Type: Thesis (Masters)
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 11 Dec 2021 10:51
Last Modified: 11 Dec 2021 10:51
URI: https://norma.ncirl.ie/id/eprint/5202

Actions (login required)

View Item View Item