Harmonic-Frontier-Audio/Subharmonic_Phonation_Vocal_Fry_Preview

Harmonic Frontier Audio – Subharmonic Phonation / Vocal Fry, Preview (v0.9)

A high-fidelity vocal dataset designed for AI training, music research, and creative sound development.

Subharmonic Phonation / Vocal Fry, a preview dataset, created by Harmonic Frontier Audio.
It provides researchers, developers, and musicians with a compact reference set demonstrating the quality, formatting, and metadata conventions used in the Harmonic Frontier Audio Extended Vocal Techniques Series.

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🔎 Summary

This dataset provides high-quality, rights-cleared audio recordings for use in AI training, music research, and sound design—capturing the rare vocal phenomenon known as subharmonic phonation (also called undertone singing or throat singing, undertone style).
It offers clean, consistent examples of this technique—where the vocal folds vibrate at subharmonic ratios of the fundamental pitch—making it valuable for expressive voice synthesis, timbre modeling, and world-vocal analysis. This dataset is focused on musical and expressive applications of subharmonic phonation, not medical or speech research contexts. Developed by Harmonic Frontier Audio, the collection follows The Proteus Standard™ for dataset provenance and ethical AI use.

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🎶 About Subharmonic Phonation

Subharmonic phonation (commonly referred to as vocal fry when produced at low frequencies) is a complex vocal technique in which the vocal folds oscillate at frequencies that are integer divisions of the fundamental pitch, creating a characteristic growling, overtone-rich sound. This technique produces a lower pitch—most commonly an octave below the sung note—by allowing the lower frequency to occur alongside the primary one.

Sometimes described as undertone singing, this phenomenon can be understood as the subharmonic counterpart to overtone singing. It appears prominently in Tuvan and Mongolian throat singing traditions, Tibetan Buddhist chant, and other practices that explore the polyphonic possibilities of the human voice.

It is also occasionally employed in Western vocal performance, experimental music, and cinematic sound design for its striking resonance and textural depth.

This dataset presents a neutral, non-traditional representation of the technique — recorded as a general-purpose study of subharmonic phonation rather than as an emulation of any specific cultural or regional style.
The intention is to provide AI researchers, sound designers, and developers with clean, controlled examples of this unique human sound source for analysis, synthesis, and expressive modeling.

If you find this dataset useful, please consider giving it a 🤍 on Hugging Face to help others discover it.

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📂 Contents

Audio Files (.wav)

• Recorded at 96 kHz / 24-bit WAV format
• Exported as mono
• Fade-ins and fade-outs of 3–5 ms applied for transient consistency
• DC offset minimized and normalized to maintain consistent loudness
• No compression, normalization, or external processing applied
• High-pass filtered at ~40 Hz to remove subsonic rumble

Categories in this Preview

1. Sustained Phonation
   • Sustained subharmonic tones across varying registers, sustained tone demonstrating the transition between subharmonic phonation and standard phonation
2. Scales
   • Scales demonstrating transitions between pitches
3. Consonants
   • Sustained pitches with repeated consonant sounds
4. Short Melodic Exercise
   • A simple original phrase demonstrating controlled transitions between pitches emphasizing the subharmonic registers

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Metadata (.csv)
Includes structured fields for file name, category, content, note pitch (when applicable), IPA vowel/consonant symbol, microphone, channel configuration, sample rate, bit depth, recording chain, and dataset version.

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🎤 Recording Notes

• Recorded in a treated studio environment using a single-mic setup:
  • Microphone: Rode NT1-A dynamic microphone
  • Recording chain: Rode NT1-A → Zoom F8n Pro
• Recorded at 96 kHz / 32-bit float, rendered as 96 kHz / 24-bit mono WAV for release.
• Room tone, breath noise, and subtle resonant shifts were preserved to maintain natural realism.

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🌈 Spectrogram Preview

Below is a spectrogram showing the layered harmonic structure and subharmonic content characteristic of this technique:

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🎧 Listen – Demonstration Track

Below is a short mixed and mastered music example featuring this dataset in context.
It illustrates how Subharmonic Phonation / Vocal Fry can be integrated into a musical arrangement, demonstrating both the expressive timbre and harmonic richness of the technique.

🎵 Track: “Subharmonic Example”
🎚️ Composer / Producer: Blake Pullen
📦 Source: Harmonic Frontier Audio – Subharmonic Phonation / Vocal Fry (Preview)

📜 This track is provided for demonstration purposes only and is not part of the dataset.
It may not be used for AI training, redistribution, or derivative works.

🪶 The instrumental accompaniment was generated using Suno’s Pro plan under a commercial license.
All vocal material and featured dataset sounds were performed, recorded, and produced by Blake Pullen for Harmonic Frontier Audio.

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⚡ Usage

This preview pack is designed for:

• Evaluation of Harmonic Frontier Audio dataset structure and fidelity
• Testing AI and DSP systems that model or classify vocal timbres
• Creative sound design and extended voice synthesis research

👉 Note: This is not a full dataset.
The complete Harmonic Frontier Audio dataset for Subharmonic Phonation / Vocal Fry will include:

• Broader pitch range and dynamic envelopes
• Additional vowel shapes and harmonic ratios
• Extended glissando and resonance control examples

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💡 Full Dataset Availability

This is a preview pack of the Subharmonic Phonation / Vocal Fry dataset.
The complete dataset — with extended dynamic, vowel, and resonance variations — will be available for licensing.

For licensing inquiries:
📩 [email protected]

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📥 How to Use This Dataset in Python

You can load the Parquet-converted version of this dataset directly with the datasets library:

from datasets import load_dataset

dataset = load_dataset(
    "Harmonic-Frontier-Audio/Subharmonic_Phonation_Vocal_Fry_Preview",
    split="train"
)

print(dataset)

⚙️ Note: Parquet conversion and load_dataset() support will be available within 2–3 days of publication.

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🔗 Explore More from Harmonic Frontier Audio

• Scottish Smallpipes (Preview)
• Highland Bagpipes (Preview)
• Irish Tin Whistle in D (Preview)
• Subharmonic Phonation / Vocal Fry (Preview)

(All datasets follow The Proteus Standard™ for ethical dataset provenance and licensing.)

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📜 License

Released under CC BY-NC 4.0.

• Free for non-commercial use, testing, and research.
• Commercial licensing available via Harmonic Frontier Audio.
• A formal rights declaration is included in this dataset bundle.

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📧 Contact

Harmonic Frontier Audio
📩 [email protected]
🌐 https://harmonicfrontieraudio.com/

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🔮 Future Roadmap

This preview release is part of the Harmonic Frontier Audio – Extended Vocal Techniques Series.
Upcoming planned datasets include:

• Overtone Singing
• Whisper Phonation
• Falsetto
• Vocal Percussion
• Growl / Metal Vocals

Over time, Harmonic Frontier Audio will expand the Extended Vocal Techniques Series alongside its Folk and World Instrument catalogs — creating the first unified library of ethical, rights-cleared world and human vocal datasets for AI training, synthesis, and sound design.

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🗒️ Release Notes

Version 0.9 (Nov. 2025) – Initial Preview Pack release for Subharmonic Phonation / Vocal Fry.
See CHANGELOG.md for detailed version history.

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Citation

If you use this dataset in your research, please cite:

Pullen, B. (2025). Subharmonic Phonation / Vocal Fry Dataset (Preview) [Data set]. Harmonic Frontier Audio. Zenodo.
https://doi.org/10.5281/zenodo.17526976

ORCID: https://orcid.org/0009-0003-4527-0178

BibTeX

@dataset{pullen_2025_subharmonic_preview,
  author       = {Blake Pullen},
  title        = {Subharmonic Phonation / Vocal Fry Dataset (Preview)},
  year         = {2025},
  publisher    = {Harmonic Frontier Audio},
  version      = {0.9},
  doi          = {10.5281/zenodo.17526976},
  url          = {https://doi.org/10.5281/zenodo.17526976}
}