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LEARNING OPTIMAL FEATURES FOR MUSIC TRANSCRIPTION
文献类型:会议
作者:Ming, Huaiping[1]  Huang, Dongyan[2]  Xie, Lei[3]  Li, Haizhou[4]  
机构:[1]School of Computer Science, Northwestern Polytechnical University, Xi'an, China
[2]Institute for Infocomm Research A STAR, Singapore, Singapore
[3]School of Computer Science, Northwestern Polytechnical University, Xi'an, China
[4]Institute for Infocomm Research A STAR, Singapore, Singapore
年:2014
通讯作者:Ming, HP (reprint author), Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
会议名称:2014 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (CHINASIP)
页码范围:105-109
会议地点:Xian, PEOPLES R CHINA
会议开始日期:2014-01-01
收录情况:EI(20152100870710)  CPCI-S(WOS:000366612600022)  
所属部门:计算机学院
人气指数:1256
浏览次数:1233
语言:中文
关键词:Semigram features; filter bank; constant-Q transform; logarithmic compression; music transcription
摘要:
This paper aims to design time-frequency representation (TFR) functions for automatic music transcription. It is desirable that the decomposition of those TFR functions are suitable for notes having variation of both pitch and spectral envelop over time. The Harmonic Adaptive Latent Component Analysis (HALCA) model adopted in this paper allows considering those two kinds of variations simultaneously. We evaluate the influence of three TFR functions including IIR, FIR filter bank semigram (FBSG) ...More
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