--- 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, v0.9) **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 = {0.9}, doi = {10.5281/zenodo.21959766}, url = {https://doi.org/10.5281/zenodo.21959766} } ```