文章基本信息

标题：An Efficient Feature Selection in Classification of Audio Files
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作者：Jayita Mitra ; Diganta Saha
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2014
卷号：4
期号：3
页码：29-38
DOI：10.5121/csit.2014.4303
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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 furtheranalysis, which represents the elements in feature vector. By extraction method we can compute anumerical representation that can be used to characterize the audio using the existing toolbox. In thisstudy Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attributewhich will separate the tuples into different classes. The pulse clarity is considered as a subjectivemeasure and it is used to calculate the gain of features of audio files. The splitting criterion isemployed in the application to identify the class or the music genre of a specific audio file fromtesting database. Experimental results indicate that by using GR the application can produce asatisfactory result for music genre classification. After dimensionality reduction best three featureshave been selected out of various features of audio file and in this technique we will get more than90% successful classification result.
关键词：Data Mining; Feature Extraction; Audio Classification; Gain Ratio; Pulse Clarity