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

Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange

► To cite this version:

Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange. EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal. 21st International Conference on Content-based Multimedia Indexing (CBMI), 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, Sep 2024, Reykjavik, Iceland. hal-04620072

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Submitted on 21 Jun 2024

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

Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange

<figure><img src="image_1.png" /></figure>

## To cite this version:

Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange. EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal. 21st International Conference on Content-based Multimedia Indexing (CBMI 2024), Sep 2024, Reykjavik, Iceland. hal-04620072

HAL Id: hal-04620072

https://hal.science/hal-04620072v1

Submitted on 21 Jun 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal

**Modan Tailleur**

Nantes Université, École Centrale Nantes
CNRS, LS2N, UMR 6004

Nantes, France
modan.tailleur@ls2n.fr

**Julien Pinquier**

IRIT, Université de Toulouse
CNRS, UT3

Toulouse, France
julien.pinquier@irit.fr

**Laurent Millot**

Institut Acte, UR 7539
ENS Louis-Lumière

Saint-Denis, France
l.millot@ens-louis-lumiere.fr

** Corsin Vogel**

ENS Louis-Lumière
Saint-Denis, France
c.vogel@ens-louis-lumiere.fr

**Mathieu Lagrange**

Nantes Université, École Centrale Nantes
CNRS, LS2N, UMR 6004

F-44000 Nantes, France
mathieu.lagrange@ls2n.fr

**Abstract**—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.

## I. INTRODUCTION

Heavy metal music, known in part for its distinctive vocal styles, often features singers who employ vocal distortion techniques to imbue their performances with unique timbres and heightened aggression. These techniques manifest as unconventional vibratory behaviors within the vocal tract and other larynx components such as the ventricular folds. The complexity of these techniques gives rise to a vast diversity of vocal timbres and production methods. Notably, the most extreme sub genres of heavy metal, such as death metal, black metal, metalcore, and grindcore, exhibit quite distinct vocal approaches.

Over time, a variety of extreme vocals taxonomies have emerged. Nieto [1][2] defined a taxonomy of the most extreme vocal techniques into three categories: the death growl (predominantly used in the death metal subgenre), the fry scream exhale (commonly associated with the metalcore subgenre), and the fry scream inhale (typically employed in grindcore and deathcore). Similarly, Sadolin [3] and Thuesen et al. [4] introduced related albeit less comprehensive taxonomies of extreme vocalizations prevalent in heavy metal music. Recent research by Hainaut [5] unveiled significant distinctions between vocal styles employed in death metal and those in black metal,

despite their apparent reliance on the same overarching technique. Furthermore, Purcell [6] describes an additional facet of metal vocalization, denoted as "sepulchral," characterized by even deeper tonalities, often rendering lyrics unintelligible. A more anatomical approach was taken by Chevaillier et al. [7] who categorized metal voices into four distinct classes, each correlated with specific anatomical regions of the oral cavity and vocal tract anatomy. Those studies will root the proposal of the taxonomy considered for this dataset, as detailed in section II.

Despite the emergence of several sound detection studies closely related to extreme heavy metal vocalization over the last two decades [8, 9, 10, 11, 12, 13], and the numerous acoustic analysis of growls and screams in heavy metal [14, 15, 16, 17, 18], the translation of this research into practical applications within the music realm remains a challenge. We believe that the study of *heavy metal* vocalization is promising specifically for vocal technique classification, which finds application in automatic content labeling for streaming platforms [2] or to enhance live performances by applying vocal effects in real-time. Furthermore, it offers the potential to advance voice distortion generative algorithms [19]. The emergence of deep learning algorithms presents an opportunity to elevate the capabilities of existing methodologies.

Unfortunately, the development of these applications encounters a significant obstacle: the scarcity of extensive and robust datasets. To address this gap, we present the Extreme Metal Vocals Dataset (EMVD)<fnref n="1" />, a comprehensive and diverse collection of extreme vocal performances. This dataset features a new taxonomy that encompasses four distinct distortion techniques across three vocal ranges, along with three vocal effects, making it broader in scope compared to existing datasets [20]. Unlike the dataset proposed by Kalbag and Lerch [20], the EMVD dataset comprises unprocessed vocal recordings devoid of musical accompaniment. It includes

<footnote n="1">¹Dataset available at: https://zenodo.org/record/8406322, in line with the open science policy of the European Union.</footnote>
# EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal

**Modan Tailleur**

Nantes Université, École Centrale Nantes
CNRS, LS2N, UMR 6004

Nantes, France
modan.tailleur@ls2n.fr

**Julien Pinquier**

IRIT, Université de Toulouse
CNRS, UT3

Toulouse, France
julien.pinquier@irit.fr

**Laurent Millot**

Institut Acte, UR 7539
ENS Louis-Lumière

Saint-Denis, France
l.millot@ens-louis-lumiere.fr

**Corsin Vogel**

ENS Louis-Lumière
Saint-Denis, France
c.vogel@ens-louis-lumiere.fr

**Mathieu Lagrange**

Nantes Université, École Centrale Nantes
CNRS, LS2N, UMR 6004

F-44000 Nantes, France
mathieu.lagrange@ls2n.fr

**Abstract**—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.

## I. INTRODUCTION

Heavy metal music, known in part for its distinctive vocal styles, often features singers who employ vocal distortion techniques to imbue their performances with unique timbres and heightened aggression. These techniques manifest as unconventional vibratory behaviors within the vocal tract and other larynx components such as the ventricular folds. The complexity of these techniques gives rise to a vast diversity of vocal timbres and production methods. Notably, the most extreme sub genres of heavy metal, such as death metal, black metal, metalcore, and grindcore, exhibit quite distinct vocal approaches.

Over time, a variety of extreme vocals taxonomies have emerged. Nieto [1][2] defined a taxonomy of the most extreme vocal techniques into three categories: the death growl (predominantly used in the death metal subgenre), the fry scream exhale (commonly associated with the metalcore subgenre), and the fry scream inhale (typically employed in grindcore and deathcore). Similarly, Sadolin [3] and Thuesen et al. [4] introduced related albeit less comprehensive taxonomies of extreme vocalizations prevalent in heavy metal music. Recent research by Hainaut [5] unveiled significant distinctions between vocal styles employed in death metal and those in black metal,

despite their apparent reliance on the same overarching technique. Furthermore, Purcell [6] describes an additional facet of metal vocalization, denoted as "sepulchral," characterized by even deeper tonalities, often rendering lyrics unintelligible. A more anatomical approach was taken by Chevaillier et al. [7] who categorized metal voices into four distinct classes, each correlated with specific anatomical regions of the oral cavity and vocal tract anatomy. Those studies will root the proposal of the taxonomy considered for this dataset, as detailed in section II.

Despite the emergence of several sound detection studies closely related to extreme heavy metal vocalization over the last two decades [8, 9, 10, 11, 12, 13], and the numerous acoustic analysis of growls and screams in heavy metal [14, 15, 16, 17, 18], the translation of this research into practical applications within the music realm remains a challenge. We believe that the study of *heavy metal* vocalization is promising specifically for vocal technique classification, which finds application in automatic content labeling for streaming platforms [2] or to enhance live performances by applying vocal effects in real-time. Furthermore, it offers the potential to advance voice distortion generative algorithms [19]. The emergence of deep learning algorithms presents an opportunity to elevate the capabilities of existing methodologies.

Unfortunately, the development of these applications encounters a significant obstacle: the scarcity of extensive and robust datasets. To address this gap, we present the Extreme Metal Vocals Dataset (EMVD)<fnref n="1" />, a comprehensive and diverse collection of extreme vocal performances. This dataset features a new taxonomy that encompasses four distinct distortion techniques across three vocal ranges, along with three vocal effects, making it broader in scope compared to existing datasets [20]. Unlike the dataset proposed by Kalbag and Lerch [20], the EMVD dataset comprises unprocessed vocal recordings devoid of musical accompaniment. It includes

<footnote n="1">¹Dataset available at: https://zenodo.org/record/8406322, in line with the open science policy of the European Union.</footnote>
contributions from 27 different vocalists, representing a substantial improvement over the Soundiron dataset [21], which comprises vocals from 5 singers. As of now, it stands as the largest and most varied dataset of its kind, tailored specifically for analyzing vocal techniques within the most extreme heavy metal subgenres. Section II introduces our novel taxonomy for extreme vocal techniques. Moving forward to section III, we provide a comprehensive overview of the dataset creation process, encompassing recording procedures and metadata collection. In order to illustrate the potential of our dataset, we present the results of a basic model<fnref n="2" /> adapted to the classification of techniques in section IV.

## II. TAXONOMY

This dataset primarily focuses on clear vocals (i.e., unsaturated vocals) and on the most extreme vocal distortion techniques that have been studied, excluding lighter distortion techniques such as the rattle, the growl, or the distortion. For a comprehensive understanding of these lighter techniques, please refer to Sadolin's taxonomy [3]. As detailed in Section I, the categorization of extreme vocal techniques within the realm of heavy metal music has historically been empirical, resulting in various taxonomies among different researchers. Taking the most important aspects of those proposals, this study introduces yet another taxonomy that aims at encompassing the various techniques while emphasizing their association with specific extreme subgenres within heavy metal. This taxonomy also breaks down into different vocal registers for each technique: high register for a voice approaching the limit of the head voice, mid register for the singer's most comfortable tonal range, and low register for a significantly lower-pitched voice<fnref n="3" />. See table I for a quick overview of the taxonomy.

### A. Vocal Techniques

The following techniques serve as alternatives to the clear voice in heavy metal music, in the sense that they're offering the ability to convey intelligible lyrics and accommodate a range of tonal variations.

1) **Black Shriek:** The Black Shriek finds widespread utilization in black metal and death metal. It is typically characterized by a higher pitch compared to the Death Growl, particularly noticeable in vowel pronunciation, producing a timbre that suggests an intermediary vowel sound between [a] (as in "cat") and [ɛ] (as in "mother") [5]. This vocal technique is notably employed by vocalists in bands such as *Marduk*, *Satyricon*, *Behemoth*, *Dimmu Borgir*, and *Darkthrone*.

2) **Death Growl:** The Death Growl is predominantly employed in death metal but also appears in thrash metal and black metal vocalisations. This technique is distinguished by an intermediate vowel sound situated between [a] and [ɔ] (as in "all") [5]. Vocalists in groups such as *Cannibal Corpse*, *Cattle*

Decapitation, Deicide, and Gorod are known for employing the death growl.

3) **Hardcore Scream:** The Hardcore Scream, associated with hardcore and grindcore, entails a vociferous shout characterized by varying degrees of voice distortion. This technique is also employed in the high vocal register in numerous black metal compositions. Noteworthy vocalists, including those in bands such as *Slipknot*, *The Dillinger Escape Plan*, *Stick To Your Guns*, *Fall In Archaea*, *Terror*, *Get The Shot*, and *Silverstein*, have employed the hardcore scream.

4) **Grind Inhale:** The Grind Inhale entails a screaming technique produced by inhaling air. While it finds application across various metal genres, it is predominantly used in grindcore, although it remains a relatively secondary technique compared to those mentioned above. This vocal technique is exemplified by vocalists in bands such as *Annotations Of An Autopsy*, *Walking The Cadaver*, and *Archspire*.

### B. Vocal Effects

Heavy metal vocalists also incorporate vocal effects to punctuate their performances. These effects are often only sparingly employed within musical compositions, as they render speech entirely unintelligible, setting them apart from the vocal techniques presented in the previous section.

1) **Pig Squeal:** The Pig Squeal technique involves imitating a pig's call and is typically characterized by high-pitched tones. Similar to the Grind Inhale, it is primarily produced by inhaling air rather than exhaling.

2) **Deep Gutturals:** The Deep Gutturals represent Death Growls executed in the extreme lower tessitura of the singer's range, aiming to achieve the lowest possible vocalization.

3) **Tunnel Throat:** The Tunnel Throat technique involves curling the tongue against the palate to produce a profoundly low-pitched growl. The resulting sound is often likened to the noise of an unblocking sink, with a pitch slightly higher than that of Deep Gutturals.

<table><caption>TABLE I
TAXONOMY OF VOCAL TECHNIQUES AND VOCAL EFFECTS</caption><thead><tr><th></th><th>Breathing method</th><th>Registers</th><th>Main metal subgenres</th></tr></thead><tbody><tr><td rowspan="4">techniques</td><td>Black Shriek</td><td>Exhale</td><td>High, Mid</td><td>black metal, death metal</td></tr><tr><td>Death Growl</td><td>Exhale</td><td>Mid, Low</td><td>death metal, thrash metal, black metal</td></tr><tr><td>Hardcore Scream</td><td>Exhale</td><td>High, Mid, Low</td><td>hardcore, grindcore</td></tr><tr><td>Grind Inhale</td><td>Inhale</td><td>High, Mid, Low</td><td>grindcore</td></tr><tr><td rowspan="3">effects</td><td>Pig Squeal</td><td>Inhale</td><td>-</td><td>-</td></tr><tr><td>Deep Gutturals</td><td>Exhale</td><td>-</td><td>-</td></tr><tr><td>Tunnel Throat</td><td>Exhale</td><td>-</td><td>-</td></tr></tbody></table>

## III. THE EMVD DATASET

### A. Recording devices

For the recording sessions, a mobile setup was selected to accommodate as many singers as possible. An SM58

<footnote n="2">²Code available at: https://github.com/modantailleur/ExtremeMetalVocalsDataset</footnote>

<footnote n="3">³Audio examples available at: https://modantailleur.github.io/ExtremeMetalVocalsDataset/</footnote>
contributions from 27 different vocalists, representing a substantial improvement over the Soundiron dataset [21], which comprises vocals from 5 singers. As of now, it stands as the largest and most varied dataset of its kind, tailored specifically for analyzing vocal techniques within the most extreme heavy metal subgenres. Section II introduces our novel taxonomy for extreme vocal techniques. Moving forward to section III, we provide a comprehensive overview of the dataset creation process, encompassing recording procedures and metadata collection. In order to illustrate the potential of our dataset, we present the results of a basic model<fnref n="2" /> adapted to the classification of techniques in section IV.

## II. TAXONOMY

This dataset primarily focuses on clear vocals (i.e., unsaturated vocals) and on the most extreme vocal distortion techniques that have been studied, excluding lighter distortion techniques such as the rattle, the growl, or the distortion. For a comprehensive understanding of these lighter techniques, please refer to Sadolin's taxonomy [3]. As detailed in Section I, the categorization of extreme vocal techniques within the realm of heavy metal music has historically been empirical, resulting in various taxonomies among different researchers. Taking the most important aspects of those proposals, this study introduces yet another taxonomy that aims at encompassing the various techniques while emphasizing their association with specific extreme subgenres within heavy metal. This taxonomy also breaks down into different vocal registers for each technique: high register for a voice approaching the limit of the head voice, mid register for the singer's most comfortable tonal range, and low register for a significantly lower-pitched voice<fnref n="3" />. See table I for a quick overview of the taxonomy.

### A. Vocal Techniques

The following techniques serve as alternatives to the clear voice in heavy metal music, in the sense that they're offering the ability to convey intelligible lyrics and accommodate a range of tonal variations.

1) **Black Shriek:** The Black Shriek finds widespread utilization in black metal and death metal. It is typically characterized by a higher pitch compared to the Death Growl, particularly noticeable in vowel pronunciation, producing a timbre that suggests an intermediary vowel sound between [a] (as in "cat") and [ɛ] (as in "mother") [5]. This vocal technique is notably employed by vocalists in bands such as *Marduk*, *Satyricon*, *Behemoth*, *Dimmu Borgir*, and *Darkthrone*.

2) **Death Growl:** The Death Growl is predominantly employed in death metal but also appears in thrash metal and black metal vocalisations. This technique is distinguished by an intermediate vowel sound situated between [a] and [ɔ] (as in "all") [5]. Vocalists in groups such as *Cannibal Corpse*, *Cattle*

Decapitation, Deicide, and Gorod are known for employing the death growl.

3) **Hardcore Scream:** The Hardcore Scream, associated with hardcore and grindcore, entails a vociferous shout characterized by varying degrees of voice distortion. This technique is also employed in the high vocal register in numerous black metal compositions. Noteworthy vocalists, including those in bands such as *Slipknot*, *The Dillinger Escape Plan*, *Stick To Your Guns*, *Fall In Archaea*, *Terror*, *Get The Shot*, and *Silverstein*, have employed the hardcore scream.

4) **Grind Inhale:** The Grind Inhale entails a screaming technique produced by inhaling air. While it finds application across various metal genres, it is predominantly used in grindcore, although it remains a relatively secondary technique compared to those mentioned above. This vocal technique is exemplified by vocalists in bands such as *Annotations Of An Autopsy*, *Walking The Cadaver*, and *Archspire*.

### B. Vocal Effects

Heavy metal vocalists also incorporate vocal effects to punctuate their performances. These effects are often only sparingly employed within musical compositions, as they render speech entirely unintelligible, setting them apart from the vocal techniques presented in the previous section.

1) **Pig Squeal:** The Pig Squeal technique involves imitating a pig's call and is typically characterized by high-pitched tones. Similar to the Grind Inhale, it is primarily produced by inhaling air rather than exhaling.

2) **Deep Gutturals:** The Deep Gutturals represent Death Growls executed in the extreme lower tessitura of the singer's range, aiming to achieve the lowest possible vocalization.

3) **Tunnel Throat:** The Tunnel Throat technique involves curling the tongue against the palate to produce a profoundly low-pitched growl. The resulting sound is often likened to the noise of an unblocking sink, with a pitch slightly higher than that of Deep Gutturals.

<table><caption>TABLE I
TAXONOMY OF VOCAL TECHNIQUES AND VOCAL EFFECTS</caption><thead><tr><th></th><th>Breathing method</th><th>Registers</th><th>Main metal subgenres</th></tr></thead><tbody><tr><td>Black Shriek</td><td>Exhale</td><td>High, Mid</td><td>black metal, death metal</td></tr><tr><td>Death Growl</td><td>Exhale</td><td>Mid, Low</td><td>death metal, thrash metal, black metal</td></tr><tr><td>Hardcore Scream</td><td>Exhale</td><td>High, Mid, Low</td><td>hardcore, grindcore</td></tr><tr><td>Grind Inhale</td><td>Inhale</td><td>High, Mid, Low</td><td>grindcore</td></tr><tr><td>Pig Squeal</td><td>Inhale</td><td>-</td><td>-</td></tr><tr><td>Deep Gutturals</td><td>Exhale</td><td>-</td><td>-</td></tr><tr><td>Tunnel Throat</td><td>Exhale</td><td>-</td><td>-</td></tr></tbody></table>

## III. THE EMVD DATASET

### A. Recording devices

For the recording sessions, a mobile setup was selected to accommodate as many singers as possible. An SM58

<footnote n="2">²Code available at: https://github.com/modantailleur/ExtremeMetalVocalsDataset</footnote>

<footnote n="3">³Audio examples available at: https://modantailleur.github.io/ExtremeMetalVocalsDataset/</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. EMVD corpus details</figcaption></figure>

<figure><img src="image_3.png" /></figure>

microphone was employed, chosen for its prevalence as a
microphone commonly used by metal singers during live
performances. A closed-back headphone served for music
playback and provided the singers with a monitor of their
own voice if they desired to hear it during recording. An
audio interface *Scarlett 6i6* by *Focusrite* was responsible for
connecting the laptop, microphone, and headset.

In some cases, singers were recorded remotely using their
own equipment (a stage microphone and an audio interface)
which are documented in the database. These singers were
provided with a video tutorial and explanatory documents to
facilitate their participation in the project, with the main author
remotely guiding them.

B. *Recording procedure*

Each singer was instructed to sustain three vowels—[a]
as in "cat," [i] as in "ship," and [u] as in "book"—for a
duration of five seconds each. They were required to maintain
a consistent pitch not only within each vowel but also across
all vowels produced. After this, they were asked to perform
for approximately 15 seconds using the same vocal technique,
but this time with lyrics of their choosing. The lyrics had to
remain the same across all technique categories. A musical
loop was provided in the singers' headphones during each
recording. Each vocal technique was recorded across several
registers (high, mid, and low) depending on their relevance to
the specific technique. If a singer was uncomfortable using a
particular technique or effect, they were explicitly asked not
to use it to prevent potential vocal strain. Notably, the Grind
Inhale technique, although producible in multiple registers,
was recorded in only one register, as many singers deemed
it potentially harmful to their voice.

Of the 27 singers involved in the project, 22 were recorded
using the equipment detailed in section III-A, while 5 singers
utilized their own remote recording setups.

C. Metadata

Based on his experience with this type of content, the main
author provided grades to individual audio files created by
the singers, ranging from 0 to 2. A 2 grade suggests that the
technique closely represents the intended vocal technique, 1

indicates that it moderately represents the vocal technique, and
0 signifies that the technique does not adequately represent the
vocal technique. Audio files rated as 0 should not be employed
for deep learning applications, but they are retained within
the dataset in case future re-evaluation of the audio files is
desired. Notably, approximately 70% of the dataset's audio
files received grades of 2 or 1 from the authors and are thus
suitable for being used in diverse applications.

D. *Statistics and examples*

Keeping only 2 and 1 graded files with the main author
grading system, the dataset comprises a total of 760 individual
audio files, which corresponds to a duration of 100 minutes
of audio recordings. Figure 1 provides a breakdown of the
recording times for each vocal technique and vocal effect.
The dataset thus consists of 38% Clear Voice recordings, 13%
Black Shriek, 21% Death Growl, 19% Hardcore Scream, and
5% Grind Inhale. Vocal effects constitute less than 2% of
the overall dataset. Figure 1 also illustrates the number of
distinct singers who participated in the recordings for each
vocal technique and vocal effect. Notably, the techniques of
Clear Voice, Black Shriek, Death Growl, and Hardcore Scream
involved recordings by more than 15 different singers each.
In contrast, the Grind Inhale technique and vocal effects were
recorded by fewer than 10 singers each. Among the 27 singers
that participated to the study, it is worth to note that only 4
were women.

Figure 2 presents an example of audio spectrogram for each
of the 5 vocal techniques and 3 vocal effects. These figures
reveal that distinguishing Clear Voice from vocal distortion
techniques is expected to be much easier than differentiating
between various distortion techniques, as the harmonics of
Clear Voice are completely altered in the other techniques.

A noteworthy comparison can be made between Grind
Inhale and Pig Squeal techniques. Despite both involving
inhaling air, Pig Squeal exhibits a significantly higher level of
noise around 2.8 kHz. This noise contributes to its resemblance
to the sound of a squealing pig.

Deep Gutturals and Tunnel Throat exhibit very similar spec-
trograms, with the primary distinction being the elevated en-
ergy in frequencies above 900 Hz, which is more pronounced
<figure><img src="image_3.png" /><figcaption>Fig. 1. EMVD corpus details</figcaption></figure>

<figure><img src="image_4.png" /></figure>

microphone was employed, chosen for its prevalence as a
microphone commonly used by metal singers during live
performances. A closed-back headphone served for music
playback and provided the singers with a monitor of their
own voice if they desired to hear it during recording. An
audio interface *Scarlett 6i6* by *Focusrite* was responsible for
connecting the laptop, microphone, and headset.

In some cases, singers were recorded remotely using their
own equipment (a stage microphone and an audio interface)
which are documented in the database. These singers were
provided with a video tutorial and explanatory documents to
facilitate their participation in the project, with the main author
remotely guiding them.

B. *Recording procedure*

Each singer was instructed to sustain three vowels—[a]
as in "cat," [i] as in "ship," and [u] as in "book"—for a
duration of five seconds each. They were required to maintain
a consistent pitch not only within each vowel but also across
all vowels produced. After this, they were asked to perform
for approximately 15 seconds using the same vocal technique,
but this time with lyrics of their choosing. The lyrics had to
remain the same across all technique categories. A musical
loop was provided in the singers' headphones during each
recording. Each vocal technique was recorded across several
registers (high, mid, and low) depending on their relevance to
the specific technique. If a singer was uncomfortable using a
particular technique or effect, they were explicitly asked not
to use it to prevent potential vocal strain. Notably, the Grind
Inhale technique, although producible in multiple registers,
was recorded in only one register, as many singers deemed
it potentially harmful to their voice.

Of the 27 singers involved in the project, 22 were recorded
using the equipment detailed in section III-A, while 5 singers
utilized their own remote recording setups.

C. Metadata

Based on his experience with this type of content, the main
author provided grades to individual audio files created by
the singers, ranging from 0 to 2. A 2 grade suggests that the
technique closely represents the intended vocal technique, 1

indicates that it moderately represents the vocal technique, and
0 signifies that the technique does not adequately represent the
vocal technique. Audio files rated as 0 should not be employed
for deep learning applications, but they are retained within
the dataset in case future re-evaluation of the audio files is
desired. Notably, approximately 70% of the dataset's audio
files received grades of 2 or 1 from the authors and are thus
suitable for being used in diverse applications.

D. *Statistics and examples*

Keeping only 2 and 1 graded files with the main author
grading system, the dataset comprises a total of 760 individual
audio files, which corresponds to a duration of 100 minutes
of audio recordings. Figure 1 provides a breakdown of the
recording times for each vocal technique and vocal effect.
The dataset thus consists of 38% Clear Voice recordings, 13%
Black Shriek, 21% Death Growl, 19% Hardcore Scream, and
5% Grind Inhale. Vocal effects constitute less than 2% of
the overall dataset. Figure 1 also illustrates the number of
distinct singers who participated in the recordings for each
vocal technique and vocal effect. Notably, the techniques of
Clear Voice, Black Shriek, Death Growl, and Hardcore Scream
involved recordings by more than 15 different singers each.
In contrast, the Grind Inhale technique and vocal effects were
recorded by fewer than 10 singers each. Among the 27 singers
that participated to the study, it is worth to note that only 4
were women.

Figure 2 presents an example of audio spectrogram for each
of the 5 vocal techniques and 3 vocal effects. These figures
reveal that distinguishing Clear Voice from vocal distortion
techniques is expected to be much easier than differentiating
between various distortion techniques, as the harmonics of
Clear Voice are completely altered in the other techniques.

A noteworthy comparison can be made between Grind
Inhale and Pig Squeal techniques. Despite both involving
inhaling air, Pig Squeal exhibits a significantly higher level of
noise around 2.8 kHz. This noise contributes to its resemblance
to the sound of a squealing pig.

Deep Gutturals and Tunnel Throat exhibit very similar spec-
trograms, with the primary distinction being the elevated en-
ergy in frequencies above 900 Hz, which is more pronounced
<figure><img src="image_4.png" /><figcaption>Fig. 2. Example of a 1s log-Mel-spectrogram for each vocal technique and effect</figcaption></figure>

in Tunnel Throat. Additionally, these frequencies appear to be modulated at a certain rate, contributing significantly to the generation of a sound reminiscent of a sink being unblocked.

Furthermore, Black Shriek appears to encompass characteristics from both Death Growl and Hardcore Scream techniques, featuring localized noise typical of Hardcore Scream as well as lower, more distorted noise akin to Death Growl.

## IV. VOCAL TECHNIQUES CLASSIFICATION

Due to limited data availability for vocal effects and the Grind Inhale technique, only Clear Voice, Hardcore Scream, Black Shriek, and Death Growl are retained for experimentation.

Two classification tasks are considered. First, we train an EfficientNet for binary classification, focusing on distinguishing Clear Voice from distorted vocals. Second, we train another EfficientNet for multi-class classification, aiming to distinguish among the four classes (Clear Voice, Black Shriek, Death Growl, Hardcore Scream).

An EfficientNet $b_0$ [22] architecture is trained on Mel spectrograms derived from 1-second audio samples sampled at 48 kHz. The spectrograms are created using a frame length of 1024 samples, a hop length of 256, 128 Mel-frequency bins, a minimum frequency of 20 Hz, and a maximum frequency of 8 kHz. Only audio files ranked with a 2 or a 1 by the authors are included. A k-fold cross-validation with 4 folds is performed, reserving 20% of the training data of each fold for validation. All audio files are normalized before processing. The model is trained with a learning rate of $10^{-3}$ with adam's optimizer using the BCE (Binary Cross-Entropy) loss for 20 epochs and a batch size of 32, which corresponds to 1680 iterations. The model associated with the best checkpoint is retained for further evaluation. Subsequently, we compute the accuracy scores of this optimal model's performance on the evaluation dataset for each cross-validation split.

Table II summarizes the performance of the model for both multi-class and binary classification, indicating typical micro and macro accuracy scores. The confusion matrix in Figure 3 further illustrates the classification results for the multi-class

<table><caption>TABLE II
ACCURACY SCORES ON CLASSIFICATION TASKS</caption><thead><tr><th></th><th>Micro Accuracy</th><th>Macro Accuracy</th></tr></thead><tbody><tr><td>Binary Classification</td><td>93%</td><td>93%</td></tr><tr><td>Multi-class classification</td><td>75%</td><td>70%</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Confusion matrix for multi-class classification (values rounded to nearest unit).</figcaption></figure>

classification. The binary classification task achieves a notable Micro Accuracy score of 93% and a Macro Accuracy score of 93%. Conversely, the multi-class classification achieves a Micro Accuracy score of 75% and a Macro Accuracy score of 70%. This discrepancy can be attributed to the inherent spectrogram characteristics, with Clear Voice being significantly easier to distinguish from the other techniques. Notably, the confusion matrix shows that the Black Shriek appears challenging to differentiate, possibly because it exhibits characteristics of both Death Growl and Hardcore Scream. Those characteristics highly depend on the singer, as some singers produced Black Shriek techniques that resemble more the Death Growl, and others that resemble more the Hardcore Scream. In a future
<figure><img src="image_5.png" /><figcaption>Fig. 2. Example of a 1s log-Mel-spectrogram for each vocal technique and effect</figcaption></figure>

in Tunnel Throat. Additionally, these frequencies appear to be modulated at a certain rate, contributing significantly to the generation of a sound reminiscent of a sink being unblocked.

Furthermore, Black Shriek appears to encompass characteristics from both Death Growl and Hardcore Scream techniques, featuring localized noise typical of Hardcore Scream as well as lower, more distorted noise akin to Death Growl.

## IV. VOCAL TECHNIQUES CLASSIFICATION

Due to limited data availability for vocal effects and the Grind Inhale technique, only Clear Voice, Hardcore Scream, Black Shriek, and Death Growl are retained for experimentation.

Two classification tasks are considered. First, we train an EfficientNet for binary classification, focusing on distinguishing Clear Voice from distorted vocals. Second, we train another EfficientNet for multi-class classification, aiming to distinguish among the four classes (Clear Voice, Black Shriek, Death Growl, Hardcore Scream).

An EfficientNet $b_0$ [22] architecture is trained on Mel spectrograms derived from 1-second audio samples sampled at 48 kHz. The spectrograms are created using a frame length of 1024 samples, a hop length of 256, 128 Mel-frequency bins, a minimum frequency of 20 Hz, and a maximum frequency of 8 kHz. Only audio files ranked with a 2 or a 1 by the authors are included. A k-fold cross-validation with 4 folds is performed, reserving 20% of the training data of each fold for validation. All audio files are normalized before processing. The model is trained with a learning rate of $10^{-3}$ with adam's optimizer using the BCE (Binary Cross-Entropy) loss for 20 epochs and a batch size of 32, which corresponds to 1680 iterations. The model associated with the best checkpoint is retained for further evaluation. Subsequently, we compute the accuracy scores of this optimal model's performance on the evaluation dataset for each cross-validation split.

Table II summarizes the performance of the model for both multi-class and binary classification, indicating typical micro and macro accuracy scores. The confusion matrix in Figure 3 further illustrates the classification results for the multi-class

<table><caption>TABLE II
ACCURACY SCORES ON CLASSIFICATION TASKS</caption><thead><tr><th></th><th>Micro Accuracy</th><th>Macro Accuracy</th></tr></thead><tbody><tr><td>Binary Classification</td><td>93%</td><td>93%</td></tr><tr><td>Multi-class classification</td><td>75%</td><td>70%</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Confusion matrix for multi-class classification (values rounded to nearest unit).</figcaption></figure>

classification. The binary classification task achieves a notable Micro Accuracy score of 93% and a Macro Accuracy score of 93%. Conversely, the multi-class classification achieves a Micro Accuracy score of 75% and a Macro Accuracy score of 70%. This discrepancy can be attributed to the inherent spectrogram characteristics, with Clear Voice being significantly easier to distinguish from the other techniques. Notably, the confusion matrix shows that the Black Shriek appears challenging to differentiate, possibly because it exhibits characteristics of both Death Growl and Hardcore Scream. Those characteristics highly depend on the singer, as some singers produced Black Shriek techniques that resemble more the Death Growl, and others that resemble more the Hardcore Scream. In a future
work, the Black Shriek audio samples could be reannotated
by expert singers to fit into the Death Growl or the Hardcore
Scream categories.

V. CONCLUSION

We hope that our EMVD datset will represent a signifi-
cant milestone in the domain of extreme metal vocalization
datasets. This extensive collection, totaling 100 minutes of
raw, background-free audio, stands as the largest and most
diverse dataset of its kind to date since it comprises 740 audio
files from 27 unique vocalists, encompassing five distinct vocal
techniques and three vocal effects.

The potential applications of this dataset are manifold. First, it can be considered as a training resource to aid aspiring vocalists in honing their skills by providing a unique tool for technique evaluation and improvement. We also anticipate that this dataset will help further the understanding and utilization of extreme metal vocalization.

In terms of audio processing research, we believe it can be a
valuable resource for pioneering new avenues of investigation,
including technique recognition for automated tagging, and
real-time vocal effect processing during live performances
based on online detection of the vocalization type.

ACKNOWLEDGMENTS

We want to thank Oriol Nieto, Geoffroy Peeters, Christophe d'Alessandro and Boris Doval for fruitful discussion. We particularly want to thank Joshua Smith for guidance for the design of the taxonomy.

REFERENCES

[1] O. Nieto, “Voice transformations for extreme vocal effects,” Mémoire de master, Universitat Pompeu Fabra, Barcelone, Espagne, 2008.

[2] —, “Unsupervised clustering of extreme vocal effects,” in *Proc. 10th Int. Conf. Advances in Quantitative Laryngology*, Cincinnati, Ohio, USA, 2013.

[3] C. Sadolin, *Complete vocal technique*. Copenhague, Danemark: Bosworth Music, 2000.

[4] M. A. Thuesen, J. McGlashan, and C. Sadolin, “Curb-
ing—the metallic mode in-between: an empirical study
qualifying and categorizing restrained sounds known as
curbing based on audio perception, laryngostroboscopic
imaging, acoustics, LTAS, and EGG,” *Journal of Voice*,
no. 5, 2017.

[5] B. Hainaut, “Des vocalités ”bestiales“ ? Caractériser les voix bruitées du black metal,” *OPTvolume*, no. 1, 2020.

[6] N. J. Purcell, *Death metal music: The passion and politics of a subculture*. Jefferson, NC, USA: McFarland & Co Inc, 2003.

[7] G. Chevaillier, R. Guilbault, J.-N. Renard, P. Herman, and P. Tran Ba Huy, “La voix ”saturée“ du chanteur rock métal, un mécanisme supraglottique performant,” *La polyclinique de phoniatrie Dr Chevaillier*, 2011.

[8] W. Huang, T. K. Chiew, H. Li, T. S. Kok, and J. Biswas, “Scream detection for home applications,” in *2010 5th IEEE Conference on Industrial Electronics and Applications*, 2010.

IEEE Conference on Industrial Electronics and Applica-
tions, 2010.

[9] C. T. Ishi, K.-I. Sakakibara, H. Ishiguro, and N. Hagita, "A method for automatic detection of vocal fry," *IEEE transactions on audio, speech, and language processing*, no. 1, 2007.

[10] N. Hayasaka, A. Kawamura, and N. Sasaoka, “Noise-
robust scream detection using band-limited spectral en-
tropy,” *AEU-International Journal of Electronics and
Communications*, 2017.

[11] J. Pohjalainen, P. Alku, and T. Kinnunen, “Shout detec-
tion in noise,” in *2011 IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP)*,
2011.

[12] M. K. Nandwana, A. Ziaei, and J. H. Hansen, “Robust unsupervised detection of human screams in noisy acoustic environments,” in *2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2015.

[13] P. Laffitte, D. Sodoyer, C. Tatkeu, and L. Girin, “Deep neural networks for automatic detection of screams and shouted speech in subway trains,” in *2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2016.

[14] E. Smialek, P. Depalle, and D. Brackett, “A spectrographic analysis of vocal techniques in extreme metal for musicological analysis,” in *ICMC*, 2012.

[15] M. Guzmán Noriega, K. Acevedo, F. Leiva, V. Ortiz, N. Hormazabal, and C. Quezada, “Aerodynamic Characteristics of Growl Voice and Reinforced Falsetto in Metal Singing,” *Journal of Voice*, 2018.

[16] K. Kato and A. Ito, “Acoustic features and auditory impressions of death growl and screaming voice,” in *2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing*, 2013.

[17] C. Eckers, D. Hütz, M. Kob, P. Murphy, D. Houben, and B. Lehnert, “Voice production in death metal singers,” *Nag/Daga*, 2009.

[18] E. Smialek, P. Depalle, and D. Brackett, “Musical aspects of vowel formants in the extreme metal voice,” *Dafx12. York. Ac. Uk*, 2012.

[19] M. Gentilucci, L. Ardaillon, and M. Liuni, “Vocal dis-
tortion and real-time processing of roughness,” in *International Computer Music Conference*, 2018.

[20] V. Kalbag and A. Lerch, “Scream detection in heavy metal music,” *Proceedings of the Sound and Music Computing Conference (SMC)*, 2022.

[21] SOUNDIRON, “Voices Of Rage,”
https://soundiron.com/products/voices-of-rage,
last viewed: 09/2023.

[22] B. Koonce and B. Koonce, “EfficientNet,” *Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization*, 2021.
work, the Black Shriek audio samples could be reannotated
by expert singers to fit into the Death Growl or the Hardcore
Scream categories.

V. CONCLUSION

We hope that our EMVD datset will represent a signifi-
cant milestone in the domain of extreme metal vocalization
datasets. This extensive collection, totaling 100 minutes of
raw, background-free audio, stands as the largest and most
diverse dataset of its kind to date since it comprises 740 audio
files from 27 unique vocalists, encompassing five distinct vocal
techniques and three vocal effects.

The potential applications of this dataset are manifold. First, it can be considered as a training resource to aid aspiring vocalists in honing their skills by providing a unique tool for technique evaluation and improvement. We also anticipate that this dataset will help further the understanding and utilization of extreme metal vocalization.

In terms of audio processing research, we believe it can be a
valuable resource for pioneering new avenues of investigation,
including technique recognition for automated tagging, and
real-time vocal effect processing during live performances
based on online detection of the vocalization type.

ACKNOWLEDGMENTS

We want to thank Oriol Nieto, Geoffroy Peeters, Christophe d'Alessandro and Boris Doval for fruitful discussion. We particularly want to thank Joshua Smith for guidance for the design of the taxonomy.

REFERENCES

[1] O. Nieto, “Voice transformations for extreme vocal effects,” Mémoire de master, Universitat Pompeu Fabra, Barcelone, Espagne, 2008.

[2] —, “Unsupervised clustering of extreme vocal effects,” in *Proc. 10th Int. Conf. Advances in Quantitative Laryngology*, Cincinnati, Ohio, USA, 2013.

[3] C. Sadolin, *Complete vocal technique*. Copenhague, Danemark: Bosworth Music, 2000.

[4] M. A. Thuesen, J. McGlashan, and C. Sadolin, “Curb-ing—the metallic mode in-between: an empirical study qualifying and categorizing restrained sounds known as curbing based on audio perception, laryngostroboscopic imaging, acoustics, LTAS, and EGG,” *Journal of Voice*, no. 5, 2017.

[5] B. Hainaut, “Des vocalités ”bestiales“ ? Caractériser les voix bruitées du black metal,” *OPTvolume!*, no. 1, 2020.

[6] N. J. Purcell, *Death metal music: The passion and politics of a subculture*. Jefferson, NC, USA: McFarland & Co Inc, 2003.

[7] G. Chevaillier, R. Guilbault, J.-N. Renard, P. Herman, and P. Tran Ba Huy, “La voix ”saturée“ du chanteur rock métal, un mécanisme supraglottique performant,” *La polyclinique de phoniatrie Dr Chevaillier*, 2011.

[8] W. Huang, T. K. Chiew, H. Li, T. S. Kok, and J. Biswas, “Scream detection for home applications,” in *2010 5th IEEE Conference on Industrial Electronics and Applications*, 2010.

[9] C. T. Ishi, K.-I. Sakakibara, H. Ishiguro, and N. Hagita, “A method for automatic detection of vocal fry,” *IEEE transactions on audio, speech, and language processing*, no. 1, 2007.

[10] N. Hayasaka, A. Kawamura, and N. Sasaoka, “Noise-robust scream detection using band-limited spectral entropy,” *AEU-International Journal of Electronics and Communications*, 2017.

[11] J. Pohjalainen, P. Alku, and T. Kinnunen, “Shout detection in noise,” in *2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2011.

[12] M. K. Nandwana, A. Ziaei, and J. H. Hansen, “Robust unsupervised detection of human screams in noisy acoustic environments,” in *2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2015.

[13] P. Laffitte, D. Sodoyer, C. Tatkeu, and L. Girin, “Deep neural networks for automatic detection of screams and shouted speech in subway trains,” in *2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2016.

[14] E. Smialek, P. Depalle, and D. Brackett, “A spectrographic analysis of vocal techniques in extreme metal for musicological analysis,” in *ICMC*, 2012.

[15] M. Guzmán Noriega, K. Acevedo, F. Leiva, V. Ortiz, N. Hormazabal, and C. Quezada, “Aerodynamic Characteristics of Growl Voice and Reinforced Falsetto in Metal Singing,” *Journal of Voice*, 2018.

[16] K. Kato and A. Ito, “Acoustic features and auditory impressions of death growl and screaming voice,” in *2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing*, 2013.

[17] C. Eckers, D. Hütz, M. Kob, P. Murphy, D. Houben, and B. Lehnert, “Voice production in death metal singers,” *Nag/Daga*, 2009.

[18] E. Smialek, P. Depalle, and D. Brackett, “Musical aspects of vowel formants in the extreme metal voice,” *Dafx12. York. Ac. Uk*, 2012.

[19] M. Gentilucci, L. Ardaillon, and M. Liuni, “Vocal distortion and real-time processing of roughness,” in *International Computer Music Conference*, 2018.

[20] V. Kalbag and A. Lerch, “Scream detection in heavy metal music,” *Proceedings of the Sound and Music Computing Conference (SMC)*, 2022.

[21] SOUNDIRON, “Voices Of Rage,” https://soundiron.com/products/voices-of-rage, last viewed: 09/2023.

[22] B. Koonce and B. Koonce, “EfficientNet,” *Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization*, 2021.