An Analytical Study on Music Listener Emotion through Logistic Regression

Md. Anzir Hossain Rafath¹ , Fatema Tuz Zohura Mim² , Md. Shafiur Rahman³

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.3 , pp.15-20, Sep-2021

Online published on Sep 30, 2021

Copyright © Md. Anzir Hossain Rafath, Fatema Tuz Zohura Mim, Md. Shafiur Rahman . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Now-a-days music listeners face immense obstacles when trying to find suitable music for a specific context. This is what actually motivated us to find a suitable solution on it. Here we presented a music classification system that provides the listener an opportunity to browse their music by mood. Mood classification while listening to music is an emerging domain of music information retrieval. This paper investigates the challenging issue of recognizing emotions while listening to music. Specifically, we formulate the music types as a regression problem to predict the human emotional state. No categorical taxonomy is used that’s why the regression approach is free of the ambiguity inherent to conventional categorical approaches. This will help to build an intuitive and contextually aware playlist generation tool. Talking of play list tool we have come to the point that most people are not sure that if their mood has been changed or not while listening to their favorite music, people tend to listen to emotional music when they deal with sorrow or disgust. In the time fear and anger they don’t listen any music. In the contrast they listen to music while having good feelings. We have achieved 78% of accuracy by taking the regression approach for the research.

Key-Words / Index Term :
Data Mining, Machine learning, Regression approach, Emotion prediction, Music analysis

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