Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction.

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Bibliographic Details
Title:	Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction.
Authors:	Sudarma, Made¹ imasudarma@gmail.com, Harsemadi, I. Gede² gedeharsemadi@yahoo.com
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Feb2017, Vol. 7 Issue 1, p486-495. 10p.
Subjects:	Information retrieval software, Decision trees, Environmental music, Music industry, Data mining, Artificial neural networks, Mathematical models
Abstract:	Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v7i1.pp486-495