메뉴 건너뛰기
Library Notice
Institutional Access
If you certify, you can access the articles for free.
Check out your institutions.
ex)Hankuk University, Nuri Motors
Log in Register Help KOR
Subject
Humanities
General Humanities History Philosophy Religious Studies Linguistics and Literature Other Humanities
Social science
General Social Science Business Management Tourism Education Military Science Library and Information Science Law Sociology Journalism and Media Studies Psychological Science Political Science and Diplomacy Geography Public Administration Economics International Trade Anthropology Other Social Science
Natural science
General Natural Science Physics Biology Life Sciences Mathematics/Statistics Astronomy Earth Science Chemistry Other Natural Science
Engineering
General Engineering Architectural Engineering Mechanical Engineering Industrial Engineering Material/Energy Engineering Electrical and Electronic Engineering Marine and Ocean Engineering Chemical Engineering Computer Science Other Engineering
Medical science
General Medical Science Nursing Science Veterinary Pharmacy Traditional Korean Medicine Basic Medicine Clinical Medicine Other Medicine
Agriculture and Fishery
General Agriculture and Fishery Agriculture Fishery Science Food Science Forestry Animal Husbandry Marine Transportation Studies Other Agriculture and Fishery
Art and Kinesiology
General Arts Design Fine Arts Cosmetology Photography Music Fashion Kinesiology Performing Media Arts Other Arts and Kinesiology
Interdisciplinary Studies
Interdisciplinary Research Science and Technology Studies Brain and Cognitive Science Technology Policy Other Interdisciplinary Studies

KOR

Investigation of Timbre-related Music Feature Learning using Separated Vocal Signals
Recommendations
Search
Questions

분리된 보컬을 활용한 음색기반 음악 특성 탐색 연구

논문 기본 정보

Type
Academic journal
Author
Seungjin Lee (SK텔레콤)
Journal
The Korean Society Of Broad Engineers JOURNAL OF BROADCAST ENGINEERING Vol.24 No.6 KCI Accredited Journals
Published
2019.11
Pages
1,024 - 1,034 (11page)

Usage

cover
📌
Topic
📖
Background
🔬
Method
🏆
Result
Investigation of Timbre-related Music Feature Learning using Separated Vocal Signals
Ask AI
Recommendations
Search
Questions

Abstract· Keywords

Report Errors
Preference for music is determined by a variety of factors, and identifying characteristics that reflect specific factors is important for music recommendations. In this paper, we propose a method to extract the singing voice related music features reflecting various musical characteristics by using a model learned for singer identification. The model can be trained using a music source containing a background accompaniment, but it may provide degraded singer identification performance. In order to mitigate this problem, this study performs a preliminary work to separate the background accompaniment, and creates a data set composed of separated vocals by using the proven model structure that appeared in SiSEC, Signal Separation and Evaluation Campaign. Finally, we use the separated vocals to discover the singing voice related music features that reflect the singer’s voice. We compare the effects of source separation against existing methods that use music source without source separation.
#Singer identification #Music recommendation #Music representation learning #Timbre-related music similarity #Deep learning

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 제안 방법
Ⅲ. 실험 결과
Ⅳ. 결론
참고문헌 (References)

References (16)

Add References
J. Park, J. Lee, J. Park, J. Ha, J. Nam, Representation Learning of Music Using Artist Labels”, Proceeding of International Society for Music Information Retrieval Conference, Paris, France, pp. 717-724, 2018. google schola B. Logan, A. Salomon, “A Music Similarity Function Based on Signal Analysis”, ICME, Tokyo, Japen, pp. 22-25, 2001. google schola H. Eghbal-Zadeh, B. Lehner, M. Schedl, G. Widmer, “I-Vectors for Timbre-Based Music Similarity and Music Artist Classification”, Proceeding of International Society for Music Information Retrieval Conference, Malaga ,Spain pp. 554-560, 2015. google schola C. I. Wang, G. Tzanetakis, Singing style investigation by residual siamese convolutional neural networks”, Proceeding of International Conference Acoustic, Speech and Signal Processing, Calgary, Canada, pp. 116-120, 2018. google schola K. Lee, J. Nam, “LEARNING A JOINT EMBEDDING SPACE OF MONOPHONIC AND MIXED MUSIC SIGNALS FOR SINGING VOICE”, Proceeding of International Society for Music Information Retrieval Conference, Delft, Netherlands, 2019. google schola

Recommendations

It is an article recommended by DBpia according to the article similarity. Check out the related articles!

Related Authors

Frequently Viewed Together

Convergence Characteristics of Contemporary Musical Vocal Techniques - Focusing on the Analysis of ‘The Girl in 14G’ -
Eun-Hye Lee Journal of the Korea Entertainment Industry Association 2021 .06
Research on the Application of Rap Music and Music Expression of Popular Songs in the 2020 Melon Chart
In-Sup Shim Journal of the Korea Entertainment Industry Association 2021 .06
The Fusion of Modern Musical Vocal Techniques in Musical Song ‘The Girl in 14G’
Eun-Hye Lee 한국엔터테인먼트산업학회 학술대회 논문집 2021 .05
Study and Application of Vocal Scale Models for Contemporary Musical Theatre Singing Education
Eun-Hye Lee Journal of the Korea Entertainment Industry Association 2021 .04
A Study on Non-Contact Vocal Instruction
Ji-Hyun Lim ,  Kyung-Won Min Journal of the Korea Entertainment Industry Association 2021 .01

Recently viewed articles

Digitization of Old Chinese Bibles(pre-1950s)
Simon Wong, Sukjoo KimKorean Bible Society2019.04
 전시보육과 도쿄의 전시탁아소를 중심으로
김경옥The Society For Asian Historical Studies2019.04
 A Study on the Developing a New "Output Requirements Model" in Interindustry Analysis: Based on the Circulation System of Production between Final Demand, Total Output, and Final Output
Gim Ho unKorea Research Institute For Human Settlements2008.06
 Clinical Outcome Associated Post-operative Acute Kidney Injury in Abdominal Surgery
Jung-Min Baw, Chang-Yeon JungThe Korean Surgical Society2020.11

Comments(0)

0

Write first comments.

UCI(KEPA) : I410-ECN-0101-2020-567-000101575