• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 2, Pages: 89-96

Original Article

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

Received Date:17 August 2022, Accepted Date:11 December 2022, Published Date:09 January 2023


Objectives: A melody is made up of several musical notes or pitches that are joined together to form one whole. This experiment aims to develop four models based on the Mel- frequency Cepstral Coefficients (MFCC) to classify the melodies played on harmonium corresponding to five different class of Assamese folk Music. Methods: The melodies of five different categories of Assamese folk songs are selected for classification. With the help of expert musicians, these melodies are played in harmonium and audio samples are recorded in the same acoustic environment. 20 MFCC’s are extracted from each of the samples and classification of the melodies is done using four supervised learning techniques- Decision Tree Classifier, Linear Discriminant Analysis (LDA), Random Forest Classifier, and Support Vector Machine (SVM). Findings: The performance of the fitted models are evaluated using different evaluation techniques and presented. A maximum of 94.17% average accuracy score is achieved under Support Vector Machine. The average accuracy scores of Decision Tree Classifier, Linear Discriminant Analysis (LDA), and Random Forest Classifier are 73.58%, 85.58%, and 86.11% respectively. The models are developed based on 250 samples (50 from each type). However, increasing the training sample size, there is a possibility to improve the performances of the other three models also. Novelty: The developed approach for identifying the melodies is based on computational techniques. This work will certainly provide a basis for conducting further computational studies in folk music for any community. Keywords: Assamese Folk Music; Decision Tree Classifier; Linear Discriminant Analysis; Random Forest Classifier; Support Vector Machine


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© 2023 Bora et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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