EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal

International audience In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 m...

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Bibliographic Details
Main Authors: Tailleur, Modan, Pinquier, Julien, Millot, Laurent, Vogel, Corsin, Lagrange, Mathieu
Other Authors: Équipe Structuration, Analyse et MOdélisation de documents Vidéo et Audio (IRIT-SAMoVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), ENS Louis Lumière, Institut ACTE - Arts Créations Théories Esthétiques (ACTE), Université Paris 1 Panthéon-Sorbonne (UP1), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Gylfi Þór Guðmundsson, Laurent Amsaleg, Omar Shahbaz Khan, Ralph Gasser, Shin’ichi Satoh, Maria Pegia, Aladine Chetouani, Björn Þór Jónsson, Claudio Gennaro, Ewa Kijak, Ilias Gialampoukidis, Liting Zhou, Jenny Benois-Pineau, Stevan Rudinac
Format: Conference Object
Language:English
Published: HAL CCSD 2024
Subjects:
Articial Intelligence
Audio
Dataset
Heavy Metal Music
Vocal Distortion
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph]
Iceland
Online Access:https://hal.science/hal-04620072
https://hal.science/hal-04620072/document
https://hal.science/hal-04620072/file/main.pdf
Description
Summary:International audience In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings are from 27 different singers and are provided without accompanying musical instruments or post-processing effects. The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects, all performed in different pitch ranges. Performance of a state-of-the-art deep learning model is evaluated for two different classification tasks related to vocal techniques, demonstrating the potential of this resource for the audio processing community.

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