​Does Data Quality Beat Data Volume? Rethinking Acoustic Training Data for Music AI

​Presenter: Blake Pullen is the founder of Harmonic Frontier Audio, a purpose-built acoustic dataset company focused on rights-cleared, articulation-level recordings for AI model training. His background spans professional performance in opera, bagpipes, and Celtic music, alongside recording engineering and audio production. These experiences directly informs how HFA approaches acoustic data capture and catalog architecture.

​What to Expect: The AI music industry has operated on a simple assumption: more data produces better models. But as the field matures and the legal landscape around training data tightens, a more nuanced question is emerging. Does data quality, consistency, and architectural depth matter more than raw volume for specialized music AI applications? This session explores that question through the lens of Harmonic Frontier Audio, a purpose-built acoustic dataset company founded on the premise that how data is captured, structured, and documented is as important as how much of it exists. Blake Pullen, founder of Harmonic Frontier Audio, will walk through the end-to-end process of building articulation-level acoustic datasets for AI model training, from controlled recording environments and phonation-level capture philosophy to metadata architecture, UUID-based provenance documentation, and the emerging aligned pair format that maps dry acoustic primitives to processed stems and finished ensemble mixes.

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