  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.
