Music transcription uses AI to convert audio recordings into sheet music or MIDI — automatically detecting notes, rhythms, chords, and instruments from audio, enabling musicians to learn songs, create arrangements, and analyze music without manual transcription.

What Is Music Transcription?

• Definition: AI conversion of audio to musical notation.
• Input: Audio recordings (MP3, WAV).
• Output: Sheet music, MIDI files, chord charts, tabs.
• Goal: Accurate note-by-note representation of music.

Transcription Tasks

Melody Transcription: Extract main tune, single-note line. Polyphonic Transcription: Multiple simultaneous notes (piano, guitar). Chord Recognition: Identify chord progressions. Drum Transcription: Detect drum hits, patterns. Multi-Instrument: Separate and transcribe each instrument.

AI Techniques

Pitch Detection: Identify fundamental frequencies, overtones. Onset Detection: Find note start times. Source Separation: Isolate instruments before transcription. Deep Learning: CNNs on spectrograms, RNNs for temporal patterns. Music Language Models: Transformers for musical context.

Challenges: Polyphonic music (multiple notes), overlapping instruments, audio quality, expressive timing, ornaments.

Applications: Learning songs, creating sheet music, music analysis, copyright detection, music education.

Tools: AnthemScore, ScoreCloud, Melodyne, Transcribe!, MuseScore, Sonic Visualiser.