World Resonance Gallery
High-fidelity kalimba notes, resonant plucks, overtones, and dynamic articulations captured for tonal percussion modeling and hybrid ambient textures.
Structured at the articulation level using documented production workflows and secured under the Proteus Standard™.
The kalimba, also known as the mbira, is a plucked idiophone consisting of tuned metal tines mounted to a resonant body. Sound is produced when individual tines are displaced and released, generating a clear attack followed by a naturally decaying tone shaped by both tine vibration and body resonance. Expressive control is achieved through variations in pluck force, angle, and timing rather than sustained airflow or continuous excitation.
Acoustically, the kalimba occupies a focused and harmonically rich timbral space characterized by pronounced transients and distinct decay profiles. Expressivity emerges through articulation timing, gesture-level interaction with the tines, and subtle resonance behavior rather than wide dynamic or pitch modulation. Used in a variety of musical and cultural contexts worldwide, the instrument’s straightforward mechanical design and well-defined excitation–decay behavior make it well suited for articulation-level analysis and modeling, where onset clarity, resonance decay, 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):
12
Total Duration (Hours):
0.1
Sample Rate (Hz):
96000
Bit Depth (Delivery):
24
Dataset Version:
v0.9
Recording Environment:
Treated Studio
Microphone Configuration:
RØDE NT1-A positioned above the instrument and oriented toward the tines (approximately 6–8 inches), with an Oktava MK-012 positioned below and oriented toward the sound hole (approximately 6–8 inches).
Performer:
Blake Pullen
Recording Dates:
Nov. 23rd, 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 kalimba (also known as mbira), captured 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 clean note attacks, controlled decay behavior, and representative melodic gestures characteristic of plucked idiophone performance, recorded in isolation to support expressive audio modeling, evaluation, and analysis workflows.
The full dataset will expand substantially on this foundation, with broader pitch coverage, extended articulation variations, and a larger corpus of recorded material reflecting the instrument’s full expressive and resonant 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 transient detail, resonance decay, and dynamic headroom during recording and editing.
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:
Kalimba (Mbira) — Newlam Wood Kalimba
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 kalimba articulations, captured in isolation to support articulation-aware modeling and analysis.
Articulations include:
Articulations are recorded without rhythmic accompaniment or harmonic layering 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:
Additional resonance interactions, extended performance gestures, and broader dynamic variation 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 experience spanning traditional instruments, extended vocal techniques, and controlled acoustic recording.
His approach to recording the kalimba for Harmonic Frontier Audio emphasizes articulation clarity, consistency across takes, and isolation of core acoustic behaviors rather than performative or stylistic presentation. This perspective supports dataset creation optimized for machine learning applications, where clean transients, predictable decay behavior, and repeatable gesture execution are essential.
As the founder of Harmonic Frontier Audio, he performs and records the initial datasets to establish a consistent technical and methodological foundation for the catalog, ensuring that capture standards, articulation taxonomy, and provenance protocols 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.
Harmonic Frontier Audio datasets are licensed directly to research teams, startups, and enterprise partners. Access models and terms vary based on use case, scale, and integration needs.
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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