Paper Title
Feature Engineering in the Context of Classification Involving Dual Mood Music

Abstract
Abstract -This paper compares feature set variations and several methods of machine learning techniques for the classification of music that may be labeled under two mood categories which will henceforth be called dual mood music. The study tests a dataset of dual mood music which was created by gathering annotations of mood from the AllMusic website. This entails the building of feature sets using three different ways: a feature set based on music principles, a feature set built using Kendall’s Tau correlation on music extracted by the jSymbolic software, and feature set built by using a decision tree as a feature selection tool prior to classification. These feature sets were then tested using SVM, Naive Bayes and decision tree algorithms, using accuracy and F-measure as metrics of performance. The best feature set in these experiments resulted with a classification accuracy of 82.0% in the initial splitting for data points and an F-measure of 81.3%, using the decision tree when classifying songs with low valence.

Author - Carlisle Dominic Lorenzo, Andrei D. Coronel

Published : Volume-10,Issue-1  ( Jan, 2023 )

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