Celtic Constellation
Expanded ornamentation set including rolls, cuts, crans, glides, and hybrid articulations for Celtic and folk generative modeling.
Structured at the articulation level using documented production workflows and secured under the Proteus Standard™.
The tin whistle is a simple fipple flute featuring an open cylindrical bore and a direct, breath-driven sound production mechanism. In this dataset, the instrument is a tin whistle in D, where airflow supplied by the performer is shaped by the fipple mouthpiece to produce a clear, stable tone. Expressive control is achieved through breath pressure management, fingering technique, and articulation timing rather than mechanical modulation or dynamic shaping.
Acoustically, the tin whistle occupies a bright and focused timbral space, with smooth pitch transitions and a relatively direct relationship between breath input and tonal response. Expressivity emerges through articulation patterns, gesture timing, and breath-driven inflection rather than wide dynamic variation. Commonly used in folk and traditional music contexts, the instrument’s straightforward physical design and sensitivity to breath and fingering make it well suited for articulation-level analysis and modeling, where clarity of onset behavior, pitch transitions, and repeatable gesture control are important.
Key technical details for this dataset — including file counts, duration, delivery format, and session context.
Planned technical specifications and recording standards for this dataset.
Total Files:
Total Files (Preview):
12
Total Duration (Hours):
0.1
Sample Rate (Hz):
96000
Bit Depth (Delivery):
24
Dataset Version:
v0.9
Recording Environment:
Treated Studio
Microphone Configuration:
Oktava MK-012 positioned 6-8 inches in front of whistle body
Performer:
Blake Pullen
Recording Dates:
Oct. 6th, 2025
Recording Location:
Las Vegas, NV
Produced using standardized capture, editing, and QC protocols with versioned metadata and Proteus-backed provenance.
An overview of what’s included in this dataset — from articulations and performance styles to session context and recording notes.
This preview dataset contains a curated subset of articulation-focused recordings from a tin whistle in D.
The material is intended to illustrate the dataset’s structural approach, capture quality, and articulation taxonomy, rather than represent the full scope of the final release.
Included recordings emphasize stable tone production, controlled note transitions, and representative melodic gestures characteristic of tin whistle performance, captured in isolation to support expressive audio modeling, evaluation, and analysis workflows.
The full dataset will expand substantially on this foundation, with broader pitch coverage, an extended articulation set, and a larger corpus of recorded material reflecting the instrument’s full expressive range.
All audio was recorded in a controlled studio environment using standardized capture, editing, and QC protocols consistent across the Harmonic Frontier Audio catalog.
Source material was captured at 32-bit float to preserve dynamic headroom and minimize quantization artifacts during editing and processing.
Final preview files are delivered as 24-bit PCM for consistency and downstream compatibility.
A single instrument was used consistently across all sessions to maintain timbral continuity and articulation stability.
Instrument details:
Tin Whistle in D — Clark Sweetone
Post-processing was limited to trimming, fade handling, and integrity checks. No creative processing, normalization, or dynamic shaping was applied beyond what was necessary for clean delivery.
A structured breakdown of the expressive building blocks in this dataset — including articulations, dynamics, transitions, and any extended techniques captured during recording.
Unlike clip- or phrase-based datasets, this dataset is structured at the articulation and gesture level. This enables interpretable control, expressive variability, and human-aligned modeling, but significantly increases production complexity and significantly limits who can produce such datasets correctly at scale.
This preview includes representative examples of core tin whistle articulations, captured in isolation to support articulation-aware modeling and analysis.
Articulations include:
Articulations are recorded without accompaniment or rhythmic framing to preserve clarity, separability, and modeling utility.
The preview dataset includes limited examples of gesture-level behavior intended to demonstrate the structure of the full dataset rather than exhaustively cover all techniques.
Gesture types include:
Extended ornamentation patterns, advanced melodic figures, and performance-driven gesture complexity will be included in the full dataset release.
A three-layer provenance and integrity framework ensuring verifiable chain-of-custody, tamper-evident delivery, and spectral fingerprinting for enterprise deployment. These layers are versioned and maintained to support long-term auditability, continuity, and enterprise compliance.
All full datasets from HFA include provenance metadata, session identifiers, and spectral integrity markers as part of The Proteus Standard™ for compliant enterprise deployment.
Captured with expert musicians and vocalists across global traditions — ensuring each dataset carries authentic nuance, human expression, and rights-managed provenance.
Blake Pullen is a multi-disciplinary musician, vocalist, and recording engineer with a background spanning traditional Celtic music, contemporary performance, and audio production.
With formal training in vocal performance and extensive experience recording acoustic instruments, Blake approaches dataset creation from both a musical and systems-oriented perspective. His work emphasizes articulation-level clarity, consistency across sessions, and recording practices designed to support long-term machine learning use rather than short-term musical presentation.
As the founder of Harmonic Frontier Audio, he performs and records the initial datasets to establish a consistent technical and musical foundation for the catalog, ensuring that capture methodology, articulation taxonomy, and provenance standards are applied rigorously from the outset.
A three-part listening benchmark: a mixed musical demo built from this dataset, the raw source clip, and an AI model’s attempt to reproduce the same prompt.
A musical demonstration created by replacing a state-of-the-art AI-generated lead instrument with original source recordings from this dataset, then arranged and mastered to preserve musical context. This approach allows direct comparison between current-generation model output and real, rights-cleared acoustic source material.
Directly from the dataset: an isolated, unprocessed example of the source recording.
An unmodified output from a current-gen AI model given the same musical prompt. Included to illustrate where today’s systems still differ from real, recorded sources.
AI model approximations generated using publicly available state-of-the-art music generation systems.
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All datasets are delivered with versioned metadata, documented workflows, and Proteus-backed integrity manifests.
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This dataset is actively being recorded and prepared. You can request early access, previews, or discuss licensing timelines.
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