| Budget Amount *help |
¥10,660,000 (Direct Cost: ¥8,200,000、Indirect Cost: ¥2,460,000)
Fiscal Year 2016: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2015: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2014: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
Our goal is to create a system that will enable a musical novice to manipulate a piece of music, which is an ambiguous and subjective media, according to his or her intentions. The main advantage of analysis by a GTTM is that it can acquire tree structures called time-span trees. A time-span tree provides a summarization of a piece of music, which can be used as the representation of an abstraction, resulting in a music retrieval system. It can also be used for performance rendering and reproducing music. The time-span tree can also be used for melody prediction and melody morphing. These systems need a GTTM analyzer that enables us to output the results obtained from analysis that are the same as those obtained by musicologists. In this study, we developed groping structure and metrical structure analyzer based on deep learning. Experimental result shows that the analyzer shows high performance. We plan to implement time-span reduction analysis on the bases of deep learning.
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