MuseTimbre: Zero-Shot Timbre Transfer by Controlling a Frozen Music Generator

Timbre transfer aims to modify the timbral identity of a musical recording while preserving its original melody and rhythm. While single-instrument timbre transfer has made substantial progress, existing approaches to multi-instrument settings commonly rely on separate-then-transfer pipelines that propagate source-separation artifacts and process the resulting stems independently. This paper proposes MixtureTT, to the best of our knowledge the first system for flexible per-stem timbre transfer directly from a polyphonic mixture. Given a mixture and a separate timbre reference for each target voice, MixtureTT jointly transfers all stems to the specified instruments. Its Joint Stem Diffusion Transformer first models cross-stem harmonic context and then refines each output under its own content and timbre conditions. All stems are generated within a single joint diffusion process rather than through separate per-stem diffusion runs. This design avoids propagating waveform-level separation errors through a cascade and produces more coherent multi-stem outputs. Despite operating under a harder input condition, MixtureTT outperforms practical separate-then-transfer pipelines on objective and subjective evaluations, while remaining competitive with single-instrument methods given ground-truth isolated stems. Results on both synthesized and real-audio SATB benchmarks further demonstrate its generalization. Controlled ablations show that jointly modeling cross-stem dependencies improves over an equivalent independent-stem variant, while the structure and timing of this coordination are also critical for effective mixture-level timbre transfer.

This paper addresses timbral ambiguity in instrument timbre transfer under fine-grained structural conditions. We argue this issue stems from instrument-specific expressive details in these conditions, which conflict with the target timbral properties. For example, imposing a violin's pitch-dominant vibrato contours onto a flute, which naturally exhibits loudness-dominant vibrato, impairs timbral fidelity. We propose AdaTT, a target-adaptive system that ensures high timbral fidelity across diverse timbre transfer scenarios within the ControlNet scheme. It selectively scales the frame-wise influence of pitch and loudness controls via text prompts to match the target instrument's identity. We also present a semi-automatic data construction pipeline to teach the model which expressive details to transform or preserve. Results show AdaTT achieves superior timbral fidelity and naturalness while retaining score-level content. Audio samples are available at https://dabinkim0.github.io/adatt/.

Neural instrument synthesizers can clone the timbre of a solo instrument, letting music producers make music with the sound they desire. However, an instrument of interest is often embedded in a fully produced, multi-instrument recording, beyond the reach of existing methods. Source separation before timbre cloning is an approach, but it may suffer from separation artifacts and is also likely a detour for the task. In this work, we propose Point-and-Play, a system that clones the timbre of a cued source directly from a polyphonic mixture and uses it to synthesize new pitches and velocities. To achieve this, we introduce a Cue-Gated Statistics Pooling (CGSP) module that extracts the timbre code of the cued instrument from adapted CLAP embeddings of the music mixture. This extracted timbre code is then used to condition a separately trained note synthesizer together with pitch and velocity inputs. Experiments show that our proposed system outperforms separate-then-clone baselines in terms of timbre fidelity and synthesis quality, achieving comparable performance with that cloned from a clean solo instrument.