Content-base Analysis for Music Classification

• Main Authors: Yi-chang Lin, 林奕昌
• Other Authors: Yu-lung Lo
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/63148912142959619645

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 99 === The music classification techniques can be discriminated into two categories — based by music feature classification and training by learning machine classification. Both have their advantages and disadvantages. For music feature classifications, most of the approaches are based on single music feature, such as melody or chord, and the accuracy is about 70% in few genres of music. However, the accuracy for classification of most music genres is lower. In this research, we study the music contents and use the multi-features of music to design equation for more accuracy music classification. Our performance study shown that more than 85%, 82%, 80%, and 73% of folk, classic, pop, and jazz music can be classified correctly, respectively, by using multi-feature of music content for classification.