Music genre classification with convolutional neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Bibliografiset tiedot
Päätekijät:	Chowdhury, Masud, Tilok, Ibnul Islam, Das, Prodipta, Chowdhury, Avoy, Anas, MD. Abdullah Al Masum
Muut tekijät:	Mostakim, Moin
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	Brac University 2022
Aiheet:	Music genre CNN Classification Feature extraction Accuracy Neural networks (Computer science)
Linkit:	http://hdl.handle.net/10361/17023

id	10361-17023
record_format	dspace
spelling	10361-170232022-07-21T21:01:34Z Music genre classification with convolutional neural network Chowdhury, Masud Tilok, Ibnul Islam Das, Prodipta Chowdhury, Avoy Anas, MD. Abdullah Al Masum Mostakim, Moin Department of Computer Science and Engineering, Brac University Music genre CNN Classification Feature extraction Accuracy Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (page 27). Today, Music is one of the effective forms of entertainment. Everyday new Music is being composed, and the quantity of Music is increasing day by day. So, it is essential to classify or categorize Music into different genre forms accurately. Classification of Music is necessary as it enables us to differentiate the Music based on the genre. The main objective of our thesis is to extract the music feature and classify or categorize Music based on the genre. The aim is to predict the genre with the help of convolutional neural networks. There are many techniques to classify genres, but convolutional neural networks give more accuracy than other techniques. The audio dataset is collected here, and the audio signal has been converted into a spectrogram. After generating a spectrogram, CNN will give predictions based on the sample provided. Our work will give improvement to various audio and music applications. We will train the CNN to provide predictions more accurately by feeding it with huge batches of data samples. Masud Chowdhury Ibnul Islam Tilok Prodipta Das Avoy Chowdhury MD. Abdullah Al Masum Anas B. Computer Science 2022-07-21T05:56:14Z 2022-07-21T05:56:14Z 2022 2022-01 Thesis ID 17101323 ID 17201058 ID 17201059 ID 17101409 ID 20141046 http://hdl.handle.net/10361/17023 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 27 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Music genre CNN Classification Feature extraction Accuracy Neural networks (Computer science)
spellingShingle	Music genre CNN Classification Feature extraction Accuracy Neural networks (Computer science) Chowdhury, Masud Tilok, Ibnul Islam Das, Prodipta Chowdhury, Avoy Anas, MD. Abdullah Al Masum Music genre classification with convolutional neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Mostakim, Moin
author_facet	Mostakim, Moin Chowdhury, Masud Tilok, Ibnul Islam Das, Prodipta Chowdhury, Avoy Anas, MD. Abdullah Al Masum
format	Thesis
author	Chowdhury, Masud Tilok, Ibnul Islam Das, Prodipta Chowdhury, Avoy Anas, MD. Abdullah Al Masum
author_sort	Chowdhury, Masud
title	Music genre classification with convolutional neural network
title_short	Music genre classification with convolutional neural network
title_full	Music genre classification with convolutional neural network
title_fullStr	Music genre classification with convolutional neural network
title_full_unstemmed	Music genre classification with convolutional neural network
title_sort	music genre classification with convolutional neural network
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17023
work_keys_str_mv	AT chowdhurymasud musicgenreclassificationwithconvolutionalneuralnetwork AT tilokibnulislam musicgenreclassificationwithconvolutionalneuralnetwork AT dasprodipta musicgenreclassificationwithconvolutionalneuralnetwork AT chowdhuryavoy musicgenreclassificationwithconvolutionalneuralnetwork AT anasmdabdullahalmasum musicgenreclassificationwithconvolutionalneuralnetwork
_version_	1814307070666604544

Music genre classification with convolutional neural network

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