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---
pretty_name: Breathing Cycles and Physiological Patterns (Preview)
license: cc-by-nc-4.0
size_categories: [n<1K]
tags:
- audio
- dataset
- vocals
- breathing
- breathing_cycles
- physiological_patterns
- breath_noise
- human_voice
- extended_vocal_techniques
- speech_research
- phonetics
- sound_design
- ai_training_data
- sampling_96khz
- bitdepth_24bit
- mono
task_categories: [other]
language: [und]
thumbnail: thumbnail.png
description: "A comprehensive collection of natural inhale–exhale cycles, emotional breaths, and physiological airflow patterns captured with clinical clarity for expressive audio modeling."
---
![Dataset Thumbnail](./thumbnail.png)
# Harmonic Frontier Audio – Breathing Cycles and Physiological Patterns (Preview, v0.95)
**A high-fidelity human vocal dataset designed for AI training, speech research, and expressive voice modeling.**
**Breathing Cycles and Physiological Patterns (Preview)**, created by **Harmonic Frontier Audio**, provides a compact reference set demonstrating the quality, formatting, and metadata conventions used in the Harmonic Frontier Audio *Human Vocality Primitives* series.
---
## 🔎 Summary
This dataset provides high-quality, rights-cleared recordings of **natural breathing cycles and physiological airflow patterns** — non-lexical respiratory behaviors captured as isolated human acoustic primitives for expressive audio modeling.
The recordings emphasize:
- relaxed and deeper inhale–exhale cycles
- slow, extended, and meditative breathing
- faster and shallow respiratory patterns
- brief inhale and exhale holds
- irregular breathing and reset breaths
These characteristics make the dataset valuable for **AI speech modeling**, **expressive voice synthesis**, **breath-aware generative audio**, **physiological sound modeling**, and **human-aligned vocal control systems**.
Developed by **Harmonic Frontier Audio**, this preview follows *The Proteus Standard™* for dataset provenance, transparency, and ethical AI use.
Learn more about the Proteus Standard → https://harmonicfrontieraudio.com/proteus-standard
Full dataset details and licensing information are available at:
https://harmonicfrontieraudio.com/datasets/breathing-cycles-physiological-patterns
If you find this dataset useful, please consider giving it a 🤍 on Hugging Face to help others discover it.
---
## 🌬️ About Breathing Cycles and Physiological Patterns
**Breathing cycles** consist of naturally coordinated inhalation and exhalation events whose rate, depth, duration, and transitions vary with physical and expressive state.
**Physiological breathing patterns** include controlled variations such as extended breaths, shallow cycles, pauses, irregular timing, and reset breaths that occur independently of lexical speech.
These phenomena are foundational to:
- naturalistic speech and voice synthesis
- breath-aware expressive audio modeling
- physiological and respiratory sound research
- embodied and multimodal agent behavior
- realistic timing and transition modeling around vocal activity
This dataset presents a **neutral, non-linguistic, non-performative representation** of human respiratory behavior.
It is not designed to encode semantic speech content, but rather to isolate **acoustic primitives** that underlie natural breathing, respiratory timing, and physiological airflow variation.
---
## 📂 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 consistency
- No compression, normalization, or creative processing applied
- High-pass filtered at ~40 Hz to remove subsonic rumble
This preview includes **3 representative audio files**, selected to demonstrate:
- natural inhale–exhale cycle behavior
- variation in breath depth and pacing
- physiological airflow and respiratory timing characteristics
---
### Metadata (.csv)
Includes structured fields for:
- file name
- sound source type
- airflow type
- phonation type
- gesture and articulation descriptors
- microphone and recording chain
- sample rate, bit depth, and dataset version
Metadata follows the **Harmonic Frontier Audio – Foundations** schema.
---
## 🎤 Recording Notes
- Recorded in a treated studio environment using a **single-mic setup**:
- **Microphone:** Rode NT1-A condenser microphone
- **Recording chain:** Rode NT1-A → Zoom F8n Pro
- Captured at **96 kHz / 32-bit float**, rendered as **96 kHz / 24-bit** mono WAV for release.
- Performer positioned approximately **3.5 inches from the microphone**, with the microphone approximately **10–15 degrees off-axis**.
- Natural respiratory dynamics, airflow texture, and subtle breath noise were preserved to retain acoustic realism.
---
## 🌈 Spectrogram Preview
Below is a spectrogram illustrating the broadband airflow energy, inhale–exhale transitions, and changing intensity envelopes characteristic of natural breathing cycles and physiological respiratory patterns:
![Spectrogram Preview](Spectrogram_Preview.png)
## ⚡ Usage
This preview pack is designed for:
- Evaluation of Harmonic Frontier Audio dataset quality and structure
- Testing AI and DSP systems that model breathing, respiratory timing, and physiological airflow
- Research in speech synthesis, expressive vocal modeling, and breath-aware audio systems
- Creative sound design involving natural human breath and respiratory texture
👉 **Note:** This is **not a full dataset**.
The complete **Breathing Cycles and Physiological Patterns** dataset includes a substantially larger set of neutral, extended, rapid, paused, irregular, and transitional breathing primitives and is available for licensing.
---
## 💡 Full Dataset Availability
This is a **preview pack** of the *Breathing Cycles and Physiological Patterns Dataset*.
The complete dataset is available for **commercial licensing**.
For licensing inquiries:
📩 info@harmonicfrontieraudio.com
---
## 📥 How to Use This Dataset in Python
You can load the Parquet-converted version of this dataset directly with the `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset(
"Harmonic-Frontier-Audio/Breathing_Cycles_and_Physiological_Patterns_Preview",
split="train"
)
print(dataset)
```
> ⚙️ *Note: Parquet conversion and `load_dataset()` support will be available within 2–3 days of publication.*
---
## 🔗 Explore More from Harmonic Frontier Audio
- [Human Vocality Primitives Series (Previews)](https://huggingface.co/collections/Harmonic-Frontier-Audio/human-vocality-primitives-previews)
- [Celtic Constellation Series (Previews)](https://huggingface.co/collections/Harmonic-Frontier-Audio/celtic-constellation-previews)
- [Extended Vocal Techniques Spectrum (Previews)](https://huggingface.co/collections/Harmonic-Frontier-Audio/extended-vocal-techniques-spectrum-previews)
- [Novelty Gems Cabinet (Previews)](https://huggingface.co/collections/Harmonic-Frontier-Audio/novelty-gems-cabinet-previews)
*(All datasets follow The Proteus Standard™ for ethical dataset provenance and licensing.)*
---
## 📜 License
Released under **[CC BY-NC 4.0](https://creativecommons.org/licenses/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
---
## 📧 Contact
Harmonic Frontier Audio
📩 info@harmonicfrontieraudio.com
🌐 https://harmonicfrontieraudio.com/
---
## 🗒️ Release Notes
**Version 0.9 (March 2026)** – Initial Preview Pack release for Breathing Cycles and Physiological Patterns.
See `CHANGELOG.md` for detailed version history.
---
Citation
If you use this dataset in your research, please cite:
Pullen, B. (2026). Breathing Cycles and Physiological Patterns Dataset (Preview) [Data set]. Harmonic Frontier Audio. Zenodo.
https://doi.org/10.5281/zenodo.21959766
ORCID: https://orcid.org/0009-0003-4527-0178
### BibTeX
```bibtex
@dataset{pullen_2026_breathingcyclesandphysiologicalpatterns_preview,
author = {Blake Pullen},
title = {Breathing Cycles and Physiological Patterns Dataset (Preview)},
year = {2026},
publisher = {Harmonic Frontier Audio},
version = {0.95},
doi = {10.5281/zenodo.21959766},
url = {https://doi.org/10.5281/zenodo.21959766}
}
```