HMM-based note onset detection of natural humming for query by humming systems = 자연스러운 흥얼 거림 거색 시스템을 위한 은닉 마르코프 모델 기반의 음의 시작 위치 추출

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In this paper, a Hidden Markov Model (HMM)-based note onset detector for Query by humming (QBH) systems is proposed. Until now, most QBH systems have restricted the user to sing each note using predefined humming syllables instead of humming in a natural fashion. This restriction is applied to induce hard onsets which allows for more accurate onset detection at the cost of unnatural interaction. The considered note onset detector allows users to either sing using predefined humming syllables or hum using gliding nasal sounds. We defined three HMMs for our dictionary using log voicing degree and pitch variance as features: the silence model, the hard note model and the soft note model. The HMM-based onset detector decodes the input signal providing note onset information used for humming transcription. Experimental results show that the proposed algorithm outperforms conventional algorithms.
Advisors
Yoo, Chang-Dongresearcher유창동researcher
Description
한국과학기술원 : 전기 및 전자공학과,
Publisher
한국과학기술원
Issue Date
2010
Identifier
419867/325007  / 020074132
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기 및 전자공학과, 2010.2, [ vii, 37 p. ]

Keywords

Soft Onsets; Note Onset Detection; Natural Humming; Query by Humming; HMM; 은닉 마르코프 모델; 연음; 음조 위치 추출; 자연 허밍; 허밍 검색

URI
http://hdl.handle.net/10203/36654
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=419867&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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