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

Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange·June 24, 2024

Summary

The Extreme Metal Vocals Dataset (EMVD) is a groundbreaking resource for heavy metal vocal analysis, containing 760 extreme vocal recordings from 27 singers, focusing on four distortion techniques (Black Shriek, Death Growl, Hardcore Scream, Grind Inhale) and three vocal effects (Pig Squeal, Deep Gutturals, Tunnel Throat). The dataset, with a unique taxonomy and no musical accompaniment, addresses the lack of extensive datasets in this genre. It spans 1-30 seconds and 100 minutes of audio, graded for suitability in deep learning. The study uses an EfficientNet model to classify Clear Voice and distorted vocals, achieving high accuracy (93% Micro and Macro Accuracy), but struggles with multi-class classification due to the blending of certain techniques. The EMVD contributes to research on vocal technique classification, voice distortion generation, and has potential applications in tagging, scream detection, and live performance processing.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

Could you please provide more specific information or context about the paper you are referring to? This will help me better understand the problem it aims to solve and whether it is a new problem or not.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to extreme vocal distortion techniques used in heavy metal music. It explores various aspects such as automatic detection of screams and shouted speech in subway trains , acoustic features and auditory impressions of death growl and screaming voice , voice production in death metal singers , and scream detection in heavy metal music . The research delves into the aerodynamic characteristics of growl voice and reinforced falsetto in metal singing , as well as the musical aspects of vowel formants in the extreme metal voice .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

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Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

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How were the experiments in the paper designed?

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What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation of extreme vocal distortion techniques in heavy metal is the Extreme Metal Vocals Dataset (EMVD) . This dataset is comprehensive and diverse, featuring a new taxonomy that includes four distinct distortion techniques across three vocal ranges and three vocal effects . The EMVD dataset is available as open-source, and it can be accessed at the following link: https://zenodo.org/record/8406322, in line with the open science policy of the European Union .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

To provide an accurate analysis, I would need more specific information about the paper, such as the title, authors, research question, methodology, and key findings. Without these details, it is challenging to assess the quality of support for the scientific hypotheses presented in the paper. If you can provide more context or specific details, I would be happy to help analyze the experiments and results in the paper.


What are the contributions of this paper?

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What work can be continued in depth?

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  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Development of new technologies or products that require detailed testing and refinement.
  4. Long-term strategic planning that involves continuous monitoring and adjustment.
  5. Educational pursuits that involve advanced study and specialization in a particular field.

If you have a specific area of work in mind, feel free to provide more details so I can offer more tailored suggestions.


Introduction
Background
Overview of extreme metal genre and vocal techniques
Importance of vocal analysis in heavy metal music
Current limitations in available datasets
Objective
To address the lack of extensive vocal datasets in extreme metal
Develop a taxonomy for vocal techniques and effects
Evaluate the potential of the EMVD for deep learning applications
Methodology
Data Collection
Source and selection of 760 extreme vocal recordings
Inclusion of four distortion techniques and three vocal effects
Range of audio duration and suitability for deep learning
Data Preprocessing
Audio segmentation (1-30 seconds)
Audio feature extraction (e.g., Mel-spectrograms)
Grading system for clarity and distortion levels
Model Development
EfficientNet for Classification
Selection of EfficientNet architecture
Clear Voice vs. Distorted Vocal Classification
Accuracy results (93% Micro and Macro Accuracy)
Multi-Class Classification Challenges
Analysis of blending techniques and accuracy limitations
Strategies for improving multi-class classification
Applications and Potential Use Cases
Vocal technique classification
Voice distortion generation
Scream detection in live performances
Tagging and music analysis tools
Conclusion
Contribution of the EMVD to the field of music analysis
Future directions for research and dataset expansion
Impact on the extreme metal community and technology integration in live events
Basic info
papers
classical physics
sound
artificial intelligence
Advanced features
Insights
What is the Extreme Metal Vocals Dataset (EMVD) primarily focused on?
What is the primary purpose of the EMVD in terms of research applications?
What are the four main distortion techniques featured in the dataset?
How many vocal recordings and singers are included in the EMVD?

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

Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange·June 24, 2024

Summary

The Extreme Metal Vocals Dataset (EMVD) is a groundbreaking resource for heavy metal vocal analysis, containing 760 extreme vocal recordings from 27 singers, focusing on four distortion techniques (Black Shriek, Death Growl, Hardcore Scream, Grind Inhale) and three vocal effects (Pig Squeal, Deep Gutturals, Tunnel Throat). The dataset, with a unique taxonomy and no musical accompaniment, addresses the lack of extensive datasets in this genre. It spans 1-30 seconds and 100 minutes of audio, graded for suitability in deep learning. The study uses an EfficientNet model to classify Clear Voice and distorted vocals, achieving high accuracy (93% Micro and Macro Accuracy), but struggles with multi-class classification due to the blending of certain techniques. The EMVD contributes to research on vocal technique classification, voice distortion generation, and has potential applications in tagging, scream detection, and live performance processing.
Mind map
Strategies for improving multi-class classification
Analysis of blending techniques and accuracy limitations
Accuracy results (93% Micro and Macro Accuracy)
Clear Voice vs. Distorted Vocal Classification
Selection of EfficientNet architecture
Tagging and music analysis tools
Scream detection in live performances
Voice distortion generation
Vocal technique classification
Multi-Class Classification Challenges
EfficientNet for Classification
Grading system for clarity and distortion levels
Audio feature extraction (e.g., Mel-spectrograms)
Audio segmentation (1-30 seconds)
Range of audio duration and suitability for deep learning
Inclusion of four distortion techniques and three vocal effects
Source and selection of 760 extreme vocal recordings
Evaluate the potential of the EMVD for deep learning applications
Develop a taxonomy for vocal techniques and effects
To address the lack of extensive vocal datasets in extreme metal
Current limitations in available datasets
Importance of vocal analysis in heavy metal music
Overview of extreme metal genre and vocal techniques
Impact on the extreme metal community and technology integration in live events
Future directions for research and dataset expansion
Contribution of the EMVD to the field of music analysis
Applications and Potential Use Cases
Model Development
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Methodology
Introduction
Outline
Introduction
Background
Overview of extreme metal genre and vocal techniques
Importance of vocal analysis in heavy metal music
Current limitations in available datasets
Objective
To address the lack of extensive vocal datasets in extreme metal
Develop a taxonomy for vocal techniques and effects
Evaluate the potential of the EMVD for deep learning applications
Methodology
Data Collection
Source and selection of 760 extreme vocal recordings
Inclusion of four distortion techniques and three vocal effects
Range of audio duration and suitability for deep learning
Data Preprocessing
Audio segmentation (1-30 seconds)
Audio feature extraction (e.g., Mel-spectrograms)
Grading system for clarity and distortion levels
Model Development
EfficientNet for Classification
Selection of EfficientNet architecture
Clear Voice vs. Distorted Vocal Classification
Accuracy results (93% Micro and Macro Accuracy)
Multi-Class Classification Challenges
Analysis of blending techniques and accuracy limitations
Strategies for improving multi-class classification
Applications and Potential Use Cases
Vocal technique classification
Voice distortion generation
Scream detection in live performances
Tagging and music analysis tools
Conclusion
Contribution of the EMVD to the field of music analysis
Future directions for research and dataset expansion
Impact on the extreme metal community and technology integration in live events
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

Could you please provide more specific information or context about the paper you are referring to? This will help me better understand the problem it aims to solve and whether it is a new problem or not.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to extreme vocal distortion techniques used in heavy metal music. It explores various aspects such as automatic detection of screams and shouted speech in subway trains , acoustic features and auditory impressions of death growl and screaming voice , voice production in death metal singers , and scream detection in heavy metal music . The research delves into the aerodynamic characteristics of growl voice and reinforced falsetto in metal singing , as well as the musical aspects of vowel formants in the extreme metal voice .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

I would be happy to help analyze a paper for you. Please provide me with the details or key points of the paper you would like me to analyze. I appreciate your request for a detailed analysis. Please provide me with the specific details or key points from the paper you are referring to so that I can assist you in comparing the characteristics and advantages of the new methods proposed in the paper with previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

To provide you with accurate information, I would need more specific details about the topic or field of research you are referring to. Could you please provide more context or specify the research topic you are interested in?


How were the experiments in the paper designed?

To provide you with a detailed answer, I would need more specific information about the paper you are referring to. Could you please provide me with the title of the paper or some key details about the experiments so I can assist you better?


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation of extreme vocal distortion techniques in heavy metal is the Extreme Metal Vocals Dataset (EMVD) . This dataset is comprehensive and diverse, featuring a new taxonomy that includes four distinct distortion techniques across three vocal ranges and three vocal effects . The EMVD dataset is available as open-source, and it can be accessed at the following link: https://zenodo.org/record/8406322, in line with the open science policy of the European Union .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

To provide an accurate analysis, I would need more specific information about the paper, such as the title, authors, research question, methodology, and key findings. Without these details, it is challenging to assess the quality of support for the scientific hypotheses presented in the paper. If you can provide more context or specific details, I would be happy to help analyze the experiments and results in the paper.


What are the contributions of this paper?

To provide a more accurate answer, could you please specify which paper you are referring to?


What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Development of new technologies or products that require detailed testing and refinement.
  4. Long-term strategic planning that involves continuous monitoring and adjustment.
  5. Educational pursuits that involve advanced study and specialization in a particular field.

If you have a specific area of work in mind, feel free to provide more details so I can offer more tailored suggestions.

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