Bi-Sampling Approach to Classify Music Mood leveraging Raga-Rasa Association in Indian Classical Music

TL;DR

This paper introduces a novel classification framework that leverages raga-rasa associations in Indian classical music to improve mood-based music recommendation systems using machine learning and audio signal processing.

Contribution

It presents a new approach utilizing raga-rasa associations for classifying music mood, enhancing recommendation accuracy in Indian classical music.

Findings

Effective raga-rasa based classification achieved

Improved mood prediction accuracy in recommendations

Potential for personalized music mood regulation

Abstract

The impact of Music on the mood or emotion of the listener is a well-researched area in human psychology and behavioral science. In Indian classical music, ragas are the melodic structure that defines the various styles and forms of the music. Each raga has been found to evoke a specific emotion in the listener. With the advent of advanced capabilities of audio signal processing and the application of machine learning, the demand for intelligent music classifiers and recommenders has received increased attention, especially in the 'Music as a service' cloud applications. This paper explores a novel framework to leverage the raga-rasa association in Indian classical Music to build an intelligent classifier and its application in music recommendation system based on user's current mood and the mood they aspire to be in.