Classification of Music Genres Based on Mel-Frequency Cepstrum Coefficients Using Deep Learning Models

Manoj Preetham, Jemimah Beulah Panga, J. Andrew, Kumudha Raimond, Hien Dang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Genre classification is indeed a vital task today since the number of songs produced on a regular basis keeps increasing. On average, around, 60,000 tracks are being uploaded per day on Spotify. So, classifying these tracks by genre is definitely an important task for every musical streaming services and platforms. Due to the high classification performance of neural network models such as convolutional neural network (CNN), multi-layer perceptron (MLP), and long short-term memory network (LSTM) are used in this work to automatically classify music into to its genres based on Mel-frequency cepstrum coefficients (MFCCs) instead of manually entering the genre. We experimented the models with the GTZAN dataset and provided a comparative analysis on the classification efficiency of deep learning models. We achieved a classification of 70.42% for our proposed CNN model which is greater than the human accuracy and over other deep learning models.

Original languageEnglish
Title of host publicationDisruptive Technologies for Big Data and Cloud Applications - Proceedings of ICBDCC 2021
EditorsJ. Dinesh Peter, Steven Lawrence Fernandes, Amir H. Alavi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages891-907
Number of pages17
ISBN (Print)9789811921766
DOIs
Publication statusPublished - 2022
EventInternational Conference on Big Data and Cloud Computing, ICBDCC 2021 - Coimbatore, India
Duration: 20-08-202121-08-2021

Publication series

NameLecture Notes in Electrical Engineering
Volume905
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Big Data and Cloud Computing, ICBDCC 2021
Country/TerritoryIndia
CityCoimbatore
Period20-08-2121-08-21

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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