arXiv:2610.29222v1 Announce Type: new Abstract: Piano transcription models have achieved very high performance in recent state-of-the-art systems, but their performance tends to degrade in ensemble settings where the piano is played alongside other instruments. While some approaches attempt to address this issue by incorporating multi-instrumental datasets, the problem remains challenging. In particular, when other instruments produce high-energy sustained tones while relatively low-energy piano notes occur simultaneously, the magnitude components of their frequency representations overlap, making it difficult to accurately distinguish piano notes. To address this issue, we propose an improved piano transcription model that jointly leverages magnitude and phase information, enabling more robust piano transcription performance in ensemble settings. We evaluate the proposed model on two piano ensemble datasets with precise MIDI note annotations, which we obtained by revisiting and extending existing datasets. Notably, we constructed a new piano concerto dataset, PCD-K, by playing a piano part on a Disklavier and recording the audio and MIDI pairs with pre-recorded orchestral tracks. Experimental results demonstrate that phase information is highly effective in improving performance, outperforming magnitude-based approaches in ensemble settings.