Training competitive music generators and search engines requires structured, precise audio annotations. We supply verified, multi-dimensional musicology datasets with millisecond-exact alignment.
Every track is resampled offline to exactly 44100Hz mono and analyzed locally using Essentia.js WASM extractors. Key and BPM details are verified directly on the PCM audio buffer rather than using subjective web estimations.
Local audio features are synchronized with active platform endpoints (Spotify API, Odesli). This merges verified local acoustics with official global database keys, track lengths, and catalog IDs for seamless database referencing.
Generative models require semantic understanding. Using LLM models guided by strict musicological parameters, we compile structured tags detailing mix arrangements, panning structures, vocal styles, and lyrical contexts.
Every timeline block contains a strict 4-sentence structure describing: 1) Foreground melody/vocals, 2) Middle-ground keys/rhythm, 3) Background bass/drums, and 4) Dynamic energy shifts.
Detailed semantic tracking of panning distributions, sidechaining, effects (reverb/delay wetness), harmonic saturation, EQ filtering, and overall fidelity standards (studio vs. Lo-Fi).
Vocals and instruments are mapped using formal contours (undulating, conjunct, disjunct, static, ascending/descending arches) allowing generative models to train on target melodic curves.
Strict QA checks guarantee 0:00 alignment, prevent cross-reference shortcuts ("same as", "repeats"), mandate specific outro closures, and verify instrument presence per-block.
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