Celtic Constellation
Traditional mouth-blown smallpipes producing a sweet, mellow tone ideal for expressive melodic modeling and Celtic generative tasks.
Structured at the articulation level using documented production workflows and secured under the Proteus Standard™.
Scottish smallpipes are a form of bagpipe featuring an open-ended chanter and a continuous airflow-driven sound production system. In this dataset, the instrument is a mouthblown set of smallpipes, in which airflow is supplied directly by the performer rather than via bellows. This configuration introduces natural breath-driven variation while maintaining a relatively stable tonal output, with expressive control emerging primarily through fingering technique, articulation timing, and breath pressure management rather than large dynamic shifts.
Acoustically, modern Scottish smallpipes occupy a focused and consistent timbral space, with smooth note transitions and a comparatively restrained dynamic range relative to other reed instruments. Expressivity is conveyed through articulation patterns, gesture timing, and ornament-driven movement rather than abrupt changes in amplitude or timbre. Traditionally used in Scottish folk and ensemble contexts, the instrument’s physical design and continuous airflow characteristics make it well suited for articulation-level analysis and modeling, where clarity of note transitions, pressure stability, and repeatable gesture behavior 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):
13
Total Duration (Hours):
0.1
Sample Rate (Hz):
96000
Bit Depth (Delivery):
24
Dataset Version:
v0.9
Recording Environment:
Treated Studio
Microphone Configuration:
Rode NT1-A above drones, Oktava MK-012 in front of chanter
Performer:
Blake Pullen
Recording Dates:
Aug. 17th, 2025 (preview subset)
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 traditional set of Scottish smallpipes in A.
The material is designed to illustrate the dataset’s structural approach, capture quality, and articulation taxonomy, rather than represent the full scope of the final release.
Included recordings focus on stable tone production, controlled note transitions, and representative melodic gestures characteristic of Scottish smallpipes, captured in isolation for use in expressive audio modeling and evaluation workflows.
The full dataset will expand significantly on these foundations, with broader pitch coverage, extended articulations, and a substantially larger corpus of recorded material.
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 full 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:
Scottish Smallpipes in A — McCallum Folk Pipes, Poly
Additional 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 Scottish smallpipes articulations, captured in isolation to support articulation-aware modeling and analysis.
Articulations include:
Articulations are recorded without accompaniment or rhythmic framing to preserve clarity and separability.
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:
More advanced ornamentation, extended melodic figures, and performance-driven gestures 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.
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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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