Classification of Indian Classical Music With Time-Series Matching Deep Learning Approach

Music is a heavenly way of expressing feelings about the world. The language of music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an understanding can be fabricated while differentiating the types...

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Bibliographic Details
Published inIEEE access Vol. 9; pp. 102041 - 102052
Main Authors Sharma, Akhilesh Kumar, Aggarwal, Gaurav, Bhardwaj, Sachit, Chakrabarti, Prasun, Chakrabarti, Tulika, Abawajy, Jemal H., Bhattacharyya, Siddhartha, Mishra, Richa, Das, Anirban, Mahdin, Hairulnizam
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Classical music
Classification
CNN
Cultural heritage
Cultural resources
DNN
Feature extraction
Gaussian Kernel
Machine learning
Mel frequency cepstral coefficient
MFCCs
Music
Polynomial Kernel
Polynomials
ResNet-50
RNN-LSTM
Scalogram
Sigmoid Kernel
Spectrogram
SVM
Time-frequency analysis
Training
VGG-16
Wavelet transforms
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Summary:Music is a heavenly way of expressing feelings about the world. The language of music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an understanding can be fabricated while differentiating the types of Indian Classical Music. Classical music is one of the essential characteristics of Indian Cultural Heritage. Indian Classical Music is divided into two major parts, i.e. Hindustani and Carnatic. Models have been sculptured and trained to classify between Hindustani and Carnatic Music. In this paper, two approaches are used to implement classification models. MFCCs are used as features and implemented models like DNN (1 Layer, 2 Layers, 3 Layers), CNN (1 Layer, 2 Layers, 3 Layers), RNN-LSTM, SVM (Sigmoid, Polynomial & Gaussian Kernel) as one approach. A 3 channels input is created by merging features like MFCC, Spectrogram and Scalogram and implemented models like VGG-16, CNN (1 Layer, 2 Layers, 3 Layers), ResNet-50 as another approach. 3 Layered CNN and RNN-LSTM model performed best among all the approaches.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3093911