Different Machine Learning Classifiers for Music Emotion Recognition
Rahul Suresh1, Soumya A2
1Rahul Suresh, Department of Master of Science in Artificial Intelligence, Boston University.
2Soumya A*, Department of Computer Science and Engineering, RV College of Engineering, Bangalore, and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2187-2191 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7833118419/2019©BEIESP | DOI: 10.35940/ijrte.D7833.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Music in an essential part of life and the emotion carried by it is key to its perception and usage. Music Emotion Recognition (MER) is the task of identifying the emotion in musical tracks and classifying them accordingly. The objective of this research paper is to check the effectiveness of popular machine learning classifiers like XGboost, Random Forest, Decision Trees, Support Vector Machine (SVM), K-Nearest-Neighbour (KNN) and Gaussian Naive Bayes on the task of MER. Using the MIREX-like dataset [17] to test these classifiers, the effects of oversampling algorithms like Synthetic Minority Oversampling Technique (SMOTE) [22] and Random Oversampling (ROS) were also verified. In all, the Gaussian Naive Bayes classifier gave the maximum accuracy of 40.33%. The other classifiers gave accuracies in between 20.44% and 38.67%. Thus, a limit on the classification accuracy has been reached using these classifiers and also using traditional musical or statistical metrics derived from the music as input features. In view of this, deep learning-based approaches using Convolutional Neural Networks (CNNs) [13] and spectrograms of the music clips for MER is a promising alternative.
Keywords: Decision Tree classifier, Gaussian Naive Bayes, K-Nearest-Neighbour (KNN), Music Classification, Music Emotion Recognition (MER), MIREX-like dataset, Random Forest classifier, Support Vector Machine (SVM), XG boost.
Scope of the Article: Machine Learning.