DocumentCode :
591115
Title :
A supervised learning Multiple-F0 estimation algorithms for computer-synthesized music
Author :
Yi Guo ; Xinyue Bai
Author_Institution :
Coll. of Autom., Univ. of Electron. Sci. & Technol. of China (UESTC), Chengdu, China
fYear :
2012
fDate :
27-29 Aug. 2012
Firstpage :
336
Lastpage :
339
Abstract :
Multiple fundamental frequency estimation, or Multiple-FO estimation, is one of the most important problem on automatic music transcription, but it has not been well resolved up to now. This paper presents a supervised pattern recognition and machine learning methods for computer-synthesized music specifically to Multiple-F0 estimation. Computer-synthesized music is almost free from similar instruments of the differences between different individuals, so it is a good research object. It can be shown in this paper that the experimental results indicate that this method has very good recognition results.
Keywords :
learning (artificial intelligence); music; pattern recognition; automatic music transcription; computer-synthesized music; machine learning methods; multiple fundamental frequency estimation; supervised learning multiple-f0 estimation algorithms; supervised pattern recognition; Automatic music transcription; Multiple-FO estimation; PCA; pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing and Networking Technology (ICCNT), 2012 8th International Conference on
Conference_Location :
Gueongju
Print_ISBN :
978-1-4673-1326-1
Type :
conf
Filename :
6418680
Link To Document :
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