EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal
<|reference_start|>EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal: 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.<|reference_end|>
@article{tailleur2024emvd,
    title={EMVD dataset: a dataset of extreme vocal distortion techniques used in
  heavy metal},
    author={Modan Tailleur, Julien Pinquier (IRIT-SAMoVA), Laurent Millot (ACTE),
  Corsin Vogel, Mathieu Lagrange (LS2N)},
    journal={21st International Conference on Content-based Multimedia Indexing
  (CBMI), Gylfi {\TH}{\'o}r Gu{\dh}mundsson; Laurent Amsaleg; Omar Shahbaz
  Khan; Ralph Gasser; Shin'ichi Satoh; Maria Pegia; Aladine Chetouani;
  Bj{\"o}rn {\TH}{\'o}r J{\'o}nsson; Claudio Gennaro; Ewa Kijak; Ilias
  Gialampoukidis; Liting Zhou; Jenny Benois-Pineau; Stevan Rudinac, Sep 2024,
  Reykjavik, Iceland},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17732},
    primaryClass={cs.SD cs.AI physics.class-ph}
}