An Efficient Feature Selection in Classification of Audio Files

TL;DR

This paper presents an efficient feature selection method using Gain Ratio for classifying audio files, achieving over 90% accuracy in music genre classification by selecting the top three features.

Contribution

The study introduces a feature selection approach with Gain Ratio that improves classification accuracy and reduces feature dimensionality in audio genre recognition.

Findings

Over 90% classification success rate.

Selected top three features for optimal results.

Gain Ratio effectively identifies relevant features.

Abstract

In this paper we have focused on an efficient feature selection method in classification of audio files. The main objective is feature selection and extraction. We have selected a set of features for further analysis, which represents the elements in feature vector. By extraction method we can compute a numerical representation that can be used to characterize the audio using the existing toolbox. In this study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute which will separate the tuples into different classes. The pulse clarity is considered as a subjective measure and it is used to calculate the gain of features of audio files. The splitting criterion is employed in the application to identify the class or the music genre of a specific audio file from testing database. Experimental results indicate that by using GR the application can produce a satisfactory result for music genre classification. After dimensionality reduction best three features have been selected out of various features of audio file and in this technique we will get more than 90% successful classification result.