  While piano music transcription models have shown high performance for solo
piano recordings, their performance degrades when applied to ensemble
recordings. This study aims to analyze the impact of different data
augmentation methods on piano transcription performance, specifically focusing
on mixing techniques applied to violin-piano ensembles. We apply mixing methods
that consider both harmonic and temporal characteristics of the audio. To
create datasets for this study, we generated the PFVN-synth dataset, which
contains 7 hours of violin-piano ensemble audio by rendering MIDI files and
corresponding labels, and also collected unaccompanied violin recordings and
mixed them with the MAESTRO dataset. We evaluated the transcription results on
both synthesized and real audio recordings datasets.
