DuET: Note-Frame Dual Encoding for Piano Performance-to-Score Transcription

Key points

• Transcribing expressive piano performances into scores requires interpreting individual note timings within an underlying metrical structure.1
• Bidirectional cross-attention connects note and frame encoders, while an autoregressive decoder alternates between their representations to predict structured score attributes.1
• We further introduce TimeRoPE and Time-Pitch Rotary Positional Encoding (TPRoPE) to establish a shared performance-time reference and explicitly encode relative temporal and pitch relationships.1
• Evaluations on ASAP, a classical piano dataset, and our curated Gangqinpu dataset, which spans multiple genres with a focus on popular piano music, demonstrate improvements in score timing and notated duration.1

Sources (2)

• [1]DuET: Note-Frame Dual Encoding for Piano Performance-to-Score Transcription
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:11 AM

  We introduce DuET, a Dual-Encoder Transformer that jointly learns note-level performance MIDI-to-score (PM2S) transcription and frame-level beat and downbeat tracking.

  Transcribing expressive piano performances into scores requires interpreting individual note timings within an underlying metrical structure.

• [2]DuET: Note-Frame Dual Encoding for Piano Performance-to-Score Transcription
  Hugging Face Daily Papers · Oct 15, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.