AI sheet music generators have revolutionized music composition, but they’re not perfect. Many users encounter frustrating limitations, from inaccurate transcriptions to robotic-sounding compositions.
In this guide, we’ll break down the most common problems musicians face with AI notation tools—and provide proven solutions to fix them.
Challenge 1: Poor Audio-to-Sheet Transcription Accuracy
? Problem: AI misinterprets pitches, rhythms, or polyphonic layers in recordings.
? Solutions:
Use clean audio sources – Isolate instruments/vocals before processing (try iZotope RX for denoising).
Slow down fast passages – Process complex solos at 70-80% speed first.
Try multiple tools – Compare results from AnthemScore, Melodyne, and Sononym.
Manual MIDI editing first – Clean up recordings in Ableton/Cubase before notation conversion.
Pro Tip: For drum transcriptions, separate stems using LALAL.ai before processing.
Challenge 2: Generic, Uninspired Compositions
? Problem: AI-generated melodies sound repetitive or lack emotional depth.
? Solutions:
Layer multiple AI outputs – Combine phrases from MuseNet, AIVA, and Soundraw for uniqueness.
Add human improvisation – Record over AI drafts, then re-transcribe.
Use advanced prompts – Instead of "jazz piano," try:
"Bill Evans-style ballad with chromatic passing chords and rubato phrasing."Edit with intention – Inject dynamics, articulations, and tempo shifts manually.
Challenge 3: Struggles with Complex Genres
? Problem: AI fails at microtonal, avant-garde, or rhythmically intricate music.
? Solutions:
Pre-process MIDI – Manually input odd time signatures (7/8, 5/4) before AI notation.
Hybrid workflows – Use AI for basic structure, then add extended techniques manually.
Specialized tools – For contemporary classical, try Arborimus (experimental notation AI).
Challenge 4: Awkward Instrumentation & Voicing
? Problem: AI assigns melodies to unsuitable instruments or writes unplayable parts.
? Solutions:
Force instrument changes – Reassign parts in MuseScore/Dorico post-generation.
Consult orchestration guides – Reference "The Study of Orchestration" (Adler) for realistic AI edits.
Limit polyphony – Generate 4-part harmony first, then expand.
Challenge 5: Copyright & Plagiarism Risks
? Problem: AI may reproduce copyrighted motifs from its training data.
? Solutions:
Run outputs through plagiarism checkers – Try PlagiarismCheck.org for music.
Modify AI-generated themes – Change intervals, rhythm, and harmony significantly.
Use ethical AI tools – Soundraw and Boomy claim royalty-free outputs.
Challenge 6: Over-Reliance on AI
? Problem: Musicians skip learning fundamental notation/theory skills.
? Solutions:
The 80/20 rule – Use AI for first drafts, then manually notate refinements.
Take notation courses – Berklee Online’s "Music Notation and Score Preparation".
Analyze AI outputs – Study how the AI constructs phrases to improve your own skills.
Expert Workflow: Balancing AI & Human Input
1?? Draft with AI (Soundraw for ideas, AnthemScore for transcriptions)
2?? Edit in notation software (MuseScore, Dorico)
3?? Humanize with live recordings or MIDI tweaks
4?? Verify with performer feedback
The Future: Smarter AI, Smarter Musicians
As AI improves (see OpenAI’s Jukedeck 2.0 rumors), challenges will lessen—but critical listening and manual refinement will always be essential.
Have you faced other AI music challenges? Share your fixes below!