Title: Music mood taxonomy generation and classification of Christian Kokborok song: an audio-based approach

Authors: Sanchali Das; M. Prakash; Saroj K. Rajak; Swapan Debbarma

Addresses: Computer Science and Engineering, NIT Agartala, Agartala, Tripura, India ' Data Science and Analytics Centre, Department of Computer Science and Engineering, Karpagam College of Engineering, Tamil Nadu, India ' Computer Science and Engineering, NIT Agartala, Agartala, Tripura, India ' Computer Science and Engineering, NIT Agartala, Agartala, Tripura, India

Abstract: Music information retrieval (MIR) is a growing field of research and mood classification is one of the applications of MIR which represents the relationship between human emotion and music. In recent decades, it is performed on Western and Indian languages like Hindi, Telegu. We are working on an under-resourced language like Kokborok. Kokborok is a regional language of the northeastern states of India and spoken in countries like Bangladesh, Myanmar, and Bhutan etc. We have developed an audio-based system for classifying modes of Kokborok song using prominent features like rhythm, intensity, and timbre. Decision tree classifier j48 is used for the classification purpose. Our dataset composed of 125 songs and clips of 30 second is being used to build the computational model that consists of four different mood clusters, and each cluster has three subclasses. We have achieved 54.4 % accuracy rate for music mood classification on the above data.

Keywords: Kokborok Christian song; decision tree-j48; mood taxonomy; Hevners adjective.

DOI: 10.1504/IJAIP.2023.135030

International Journal of Advanced Intelligence Paradigms, 2023 Vol.26 No.2, pp.197 - 209

Received: 10 May 2018
Accepted: 10 Nov 2018
Published online: 28 Nov 2023 *

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