AI Insight
This paper introduces CPR (Composer-Performer-Refiner), a new framework for converting piano MIDI files into realistic audio that matches a reference recording's timbre. The system uses continuous autoregressive modeling where the Composer predicts musical structure, the Performer generates 24kHz audio through localized flow matching, and the Refiner upsamples to 48kHz. The approach claims to combine the temporal modeling strengths of autoregressive models with the acoustic quality of flow matching methods while avoiding information loss from quantization and reducing computational costs.
Why it matters
This advancement could improve music production tools, enabling more realistic synthesis of piano performances from digital scores while preserving the acoustic characteristics of specific instruments or recordings. The reduced computational requirements may make high-quality piano rendering more accessible for real-time applications and resource-constrained environments.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Prompt-conditioned piano MIDI-to-Music rendering aims to faithfully render target notes while reproducing the timbre of a reference recording. Existing approaches primarily follow two paradigms: autoregressive (AR) modeling and flow matching (or diffusion). Discrete-codec AR models provide causal temporal modeling, but quantization can discard acoustic detail. Flow matching better preserves acoustic structure in the cost of full-sequence attention costs and worse semantic structure. Continuous autoregressive models operate directly on continuous representations. It not only combines the condition-following ability of AR models and distribution-modeling capacity of flow matching but also bypasses the quantization bottleneck with lower computational costs. Building on this principle, we present Composer–Performer–Refiner (CPR) framework. Composer autoregressively predicts continuous hidden states, Performer generates 24kHz acoustic latents through local flow matching and Refiner then upsamples the waveform to 48 kHz. We further introduce Bottlenecked Representation Alignment (BREPA) and Modality–Time RoPE (MT-RoPE) to strengthen musical semantic structure in Composer hidden states and temporal alignments across modalities. Codes are available at https://github.com/FEAfeatherTHER/CPR_official