Mostakim, MoinChowdhury, MasudTilok, Ibnul IslamDas, ProdiptaChowdhury, AvoyAnas, MD. Abdullah Al Masum2022-07-212022-07-2120222022-01ID 17101323ID 17201058ID 17201059ID 17101409ID 20141046http://hdl.handle.net/10361/17023Cataloged from PDF version of thesis.Includes bibliographical references (page 27).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.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.27 pagesenBrac 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.Music genreCNNClassificationFeature extractionAccuracyNeural networks (Computer science)Thesis