Expert Music Transcription Data for AI Training

We deliver in Sibelius, Dorico, Finale, Guitar Pro, MuseScore, MusicXML, MIDI, and PDF. We can also adapt to custom format requirements your ML pipeline needs — just share your specification.

Every transcription is created by a professional musician with formal training in music theory and notation. We apply internal proofreading to every sample and can implement inter-annotator agreement protocols for critical datasets. Our baseline accuracy rate is 99% at the note level.

Absolutely. We regularly work with client-specific annotation guidelines covering note-level detail, metadata tagging, encoding conventions, and edge-case handling rules. We onboard to your guidelines before the pilot phase.

Any source: audio recordings, scanned sheet music, MIDI files, PDF scores, handwritten manuscripts, or synthetic audio. We handle monophonic and polyphonic sources across all genres and instrumentation levels.

We can scale to thousands of annotated samples per month with consistent quality. For large datasets, we assign dedicated annotators trained on your specific guidelines, with batch delivery schedules and quality metrics reporting.

Every sample undergoes at least one round of internal proofreading by a second annotator. For datasets requiring higher confidence, we offer multi-annotator review with majority voting and disagreement resolution. We provide QA metrics with every delivery.

Yes. We recommend starting with a small pilot batch so you can evaluate annotation quality, format compatibility, and turnaround before committing to a larger engagement. Pilot pricing is available, and pilot samples are production-quality.

Yes. We can create aligned audio-notation pairs where each audio segment is matched to its corresponding notation annotation. This includes beat-level and note-level alignment where required. Essential for AMT and audio-symbolic alignment research.