Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction.
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| Title: | Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction. |
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| 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] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 129311211 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sudarma%2C+Made%22">Sudarma, Made</searchLink><relatesTo>1</relatesTo><i> imasudarma@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Harsemadi%2C+I%2E+Gede%22">Harsemadi, I. Gede</searchLink><relatesTo>2</relatesTo><i> gedeharsemadi@yahoo.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Feb2017, Vol. 7 Issue 1, p486-495. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Information+retrieval+software%22">Information retrieval software</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+music%22">Environmental music</searchLink><br /><searchLink fieldCode="DE" term="%22Music+industry%22">Music industry</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v7i1.pp486-495 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 486 Subjects: – SubjectFull: Information retrieval software Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Environmental music Type: general – SubjectFull: Music industry Type: general – SubjectFull: Data mining Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sudarma, Made – PersonEntity: Name: NameFull: Harsemadi, I. Gede IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 7 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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