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http://dx.doi.org/10.5391/JKIIS.2010.20.6.884

Development of Music Classification of Light and Shade using VCM and Beat Tracking  

Park, Seung-Min (중앙대학교 전자전기공학부)
Park, Jun-Heong (중앙대학교 전자전기공학부)
Lee, Young-Hwan (중앙대학교 전자전기공학부)
Ko, Kwang-Eun (중앙대학교 전자전기공학부)
Sim, Kwee-Bo (중앙대학교 전자전기공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.6, 2010 , pp. 884-889 More about this Journal
Abstract
Recently, a music genre classification has been studied. However, experts use different criteria to classify each of these classifications is difficult to derive accurate results. In addition, when the emergence of a new genre of music genre is a newly re-defined. Music as a genre rather than to separate search should be classified as emotional words. In this paper, the feelings of people on the basis of brightness and darkness tries to categorize music. The proposed classification system by applying VCM(Variance Considered Machines) is the contrast of the music. In this paper, we are using three kinds of musical characteristics. Based on surveys made throughout the learning, based on musical attributes(beat, timbre, note) was used to study in the VCM. VCM is classified by the trained compared with the results of the survey were analyzed. Note extraction using the MATLAB, sampled at regular intervals to share music via the FFT frequency analysis by the sector average is defined as representing the element extracted note by quantifying the height of the entire distribution was identified. Cumulative frequency distribution in the entire frequency rage, using the difference in Timbre and were quantified. VCM applied to these three characteristics with the experimental results by comparing the survey results to see the contrast of the music with a probability of 95.4% confirmed that the two separate.
Keywords
Intelligent Musical Fountain; Support Vector Machine; Variance Considered Machines; Light and Shade; Emotion; Music Analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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