The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
히든싱어는 대한민국의 텔레비전 프로그램이다. 이날 방송에서는 원조가수와 모방가수 1명이 스크린 뒤에 숨어 노래를 부른다. 관객과 시청자는 노래하는 목소리를 듣고 원곡자가 누구인지 추측하려고 한다. 일반적으로 모방자는 잘 훈련되고 고도로 숙련되어 있기 때문에 청중의 정답은 거의 없습니다. 우리는 원래 가수와 모방 가수를 구별하기 위한 컴퓨터화된 시스템을 제안합니다. 훈련 단계에서 시스템은 원곡이 청중이 이전에 들어본 노래이기 때문에 그 노래만 학습합니다. 테스트 단계에서는 후보 1명의 노래가 시스템에 제공되고 시스템은 원곡 가수를 결정합니다. 시스템은 주체모델만 만드는 63.33등급 인증 방식을 사용한다. 주제 모델은 후보곡 간의 유사성을 측정하는 데 사용됩니다. 이 문제에서는 아티스트 식별이 필요한 기존 연구와 달리 학습 단계에서 모방자의 노래와 레이블이 제공되지 않기 때문에 다중 클래스 분류기와 지도 학습을 활용할 수 없습니다. 따라서 우리는 고도로 숙련된 모방자 중에서 원조 가수를 구별하는 데 어느 것이 더 효율적인지 선택하기 위해 여러 1-클래스 학습 알고리즘의 성능을 평가합니다. 실험 결과, 오토인코더를 적용한 제안 시스템은 다른 50-클래스 학습 알고리즘인 GMM(Gaussian Mixture Model)(26.67%)과 OCSVM(One Class Support Vector Machine)(63.33%)보다 우수한 성능(33.48%)을 보였다. 또한 제안된 시스템의 성능을 인간의 인식과 비교하기 위해 인간 콘테스트를 실시합니다. 제안한 시스템의 정확도(XNUMX%)는 인간의 평균 인지 정확도(XNUMX%)보다 우수한 것으로 나타났다.
Hosung PARK
Kongju National University
Seungsoo NAM
Kongju National University
Eun Man CHOI
Dongguk University
Daeseon CHOI
Kongju National University
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부
Hosung PARK, Seungsoo NAM, Eun Man CHOI, Daeseon CHOI, "Hidden Singer: Distinguishing Imitation Singers Based on Training with Only the Original Song" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 3092-3101, December 2018, doi: 10.1587/transinf.2018EDP7140.
Abstract: Hidden Singer is a television program in Korea. In the show, the original singer and four imitating singers sing a song in hiding behind a screen. The audience and TV viewers attempt to guess who the original singer is by listening to the singing voices. Usually, there are few correct answers from the audience, because the imitators are well trained and highly skilled. We propose a computerized system for distinguishing the original singer from the imitating singers. During the training phase, the system learns only the original singer's song because it is the one the audience has heard before. During the testing phase, the songs of five candidates are provided to the system and the system then determines the original singer. The system uses a 1-class authentication method, in which only a subject model is made. The subject model is used for measuring similarities between the candidate songs. In this problem, unlike other existing studies that require artist identification, we cannot utilize multi-class classifiers and supervised learning because songs of the imitators and the labels are not provided during the training phase. Therefore, we evaluate the performances of several 1-class learning algorithms to choose which one is more efficient in distinguishing an original singer from among highly skilled imitators. The experiment results show that the proposed system using the autoencoder performs better (63.33%) than other 1-class learning algorithms: Gaussian mixture model (GMM) (50%) and one class support vector machines (OCSVM) (26.67%). We also conduct a human contest to compare the performance of the proposed system with human perception. The accuracy of the proposed system is found to be better (63.33%) than the average accuracy of human perception (33.48%).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7140/_p
부
@ARTICLE{e101-d_12_3092,
author={Hosung PARK, Seungsoo NAM, Eun Man CHOI, Daeseon CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Hidden Singer: Distinguishing Imitation Singers Based on Training with Only the Original Song},
year={2018},
volume={E101-D},
number={12},
pages={3092-3101},
abstract={Hidden Singer is a television program in Korea. In the show, the original singer and four imitating singers sing a song in hiding behind a screen. The audience and TV viewers attempt to guess who the original singer is by listening to the singing voices. Usually, there are few correct answers from the audience, because the imitators are well trained and highly skilled. We propose a computerized system for distinguishing the original singer from the imitating singers. During the training phase, the system learns only the original singer's song because it is the one the audience has heard before. During the testing phase, the songs of five candidates are provided to the system and the system then determines the original singer. The system uses a 1-class authentication method, in which only a subject model is made. The subject model is used for measuring similarities between the candidate songs. In this problem, unlike other existing studies that require artist identification, we cannot utilize multi-class classifiers and supervised learning because songs of the imitators and the labels are not provided during the training phase. Therefore, we evaluate the performances of several 1-class learning algorithms to choose which one is more efficient in distinguishing an original singer from among highly skilled imitators. The experiment results show that the proposed system using the autoencoder performs better (63.33%) than other 1-class learning algorithms: Gaussian mixture model (GMM) (50%) and one class support vector machines (OCSVM) (26.67%). We also conduct a human contest to compare the performance of the proposed system with human perception. The accuracy of the proposed system is found to be better (63.33%) than the average accuracy of human perception (33.48%).},
keywords={},
doi={10.1587/transinf.2018EDP7140},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - Hidden Singer: Distinguishing Imitation Singers Based on Training with Only the Original Song
T2 - IEICE TRANSACTIONS on Information
SP - 3092
EP - 3101
AU - Hosung PARK
AU - Seungsoo NAM
AU - Eun Man CHOI
AU - Daeseon CHOI
PY - 2018
DO - 10.1587/transinf.2018EDP7140
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - Hidden Singer is a television program in Korea. In the show, the original singer and four imitating singers sing a song in hiding behind a screen. The audience and TV viewers attempt to guess who the original singer is by listening to the singing voices. Usually, there are few correct answers from the audience, because the imitators are well trained and highly skilled. We propose a computerized system for distinguishing the original singer from the imitating singers. During the training phase, the system learns only the original singer's song because it is the one the audience has heard before. During the testing phase, the songs of five candidates are provided to the system and the system then determines the original singer. The system uses a 1-class authentication method, in which only a subject model is made. The subject model is used for measuring similarities between the candidate songs. In this problem, unlike other existing studies that require artist identification, we cannot utilize multi-class classifiers and supervised learning because songs of the imitators and the labels are not provided during the training phase. Therefore, we evaluate the performances of several 1-class learning algorithms to choose which one is more efficient in distinguishing an original singer from among highly skilled imitators. The experiment results show that the proposed system using the autoencoder performs better (63.33%) than other 1-class learning algorithms: Gaussian mixture model (GMM) (50%) and one class support vector machines (OCSVM) (26.67%). We also conduct a human contest to compare the performance of the proposed system with human perception. The accuracy of the proposed system is found to be better (63.33%) than the average accuracy of human perception (33.48%).
ER -