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Singing Voice Detection: A Survey

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody tr...

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Detalles Bibliográficos
Autores principales: Monir, Ramy, Kostrzewa, Daniel, Mrozek, Dariusz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775013/
https://www.ncbi.nlm.nih.gov/pubmed/35052140
http://dx.doi.org/10.3390/e24010114
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author Monir, Ramy
Kostrzewa, Daniel
Mrozek, Dariusz
author_facet Monir, Ramy
Kostrzewa, Daniel
Mrozek, Dariusz
author_sort Monir, Ramy
collection PubMed
description Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection.
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spelling pubmed-87750132022-01-21 Singing Voice Detection: A Survey Monir, Ramy Kostrzewa, Daniel Mrozek, Dariusz Entropy (Basel) Review Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection. MDPI 2022-01-12 /pmc/articles/PMC8775013/ /pubmed/35052140 http://dx.doi.org/10.3390/e24010114 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Monir, Ramy
Kostrzewa, Daniel
Mrozek, Dariusz
Singing Voice Detection: A Survey
title Singing Voice Detection: A Survey
title_full Singing Voice Detection: A Survey
title_fullStr Singing Voice Detection: A Survey
title_full_unstemmed Singing Voice Detection: A Survey
title_short Singing Voice Detection: A Survey
title_sort singing voice detection: a survey
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775013/
https://www.ncbi.nlm.nih.gov/pubmed/35052140
http://dx.doi.org/10.3390/e24010114
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