digital audio workstation makers

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.

Abstract: We propose a framework for the alignment and interpolation of pitch-aligned time-frequency representations. Building on the tonal interval vector, we introduce a series of extensions that reformulate it as an invertible operator, culminating in a new feature extractor that embeds perceptual consonance priors within a pitch-aligned representation. Accordingly, we develop methods for aligning and interpolating between musical structures. For alignment, we cast the problem as a permutation search under perceptually weighted distances, enabling robust matching without explicit key detection, which is inherently ambiguous in music information retrieval. For interpolation, we employ optimal transport to generate musically meaningful transitions between pitch distributions, proposing a circular formulation over the Circle of Fourths/Fifths to respect harmonic structure. Finally, we learn a low-dimensional geometric representation of scale structure aimed to factorize scale color and density. Experimental results demonstrate the effectiveness and musicality of the proposed methods. Together, our contributions provide a principled, purely signal processing approach to modeling pitch structure in audio and symbolic music signals.