mlsanityData to 7 Oct 2026

Remix the Timbre: Diffusion-Based Style Transfer Across Polyphonic Stems

arXiv 2605.09259 ↗First posted 10 May 2026cs.SD cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Timbre transfer aims to modify the timbral identity of a musical recording while preserving the original melody and rhythm. While single-instrument timbre transfer has made substantial progress, existing approaches to multi-instrument settings rely on separate-then-transfer pipelines that propagate source separation artifacts and produce incoherent synthesized timbres across stems. 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 through a shared diffusion process. Modeling the dependencies across the per-stem content and cross-stem harmonic, the proposed joint stem diffusion transformer eliminates cascaded separation error, reduces inference cost by a factor equal to the number of stems, and yields more coherent multi-stem outputs. Despite operating under a strictly harder input condition, evaluations on the SATB choral dataset show that MixtureTT outperforms single-instrument baselines on both objective and subjective metrics demonstrating the necessity of dedicated multi-instrument timbre transfer over the naive separate-then-transfer pipelines. As a result, this work confirms that the cross-stem modeling is essential for mixture-level timbre transfer as the proposed joint setting consistently exceeds an equivalent single-stem ablation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 38 references that are in the map. First posted on arXiv 10 May 2026.

Builds on

31 of 38 references.

References · 31

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2022Scalable Diffusion Models with Transformerscited by 5,931
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  7. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  8. 2023AudioLDM: Text-to-Audio Generation with Latent Diffusion Modelscited by 592
  9. 2023High-Fidelity Audio Compression with Improved RVQGANcited by 423
  10. 2017Neural Audio Synthesis of Musical Notes with WaveNet Autoencoderscited by 222
  11. 2018Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithmscited by 221
  12. 2024Stable Audio Opencited by 192
19 more
  1. 2022Hybrid Transformers for Music Source Separationcited by 134
  2. 2023UniAudio: An Audio Foundation Model Toward Universal Audio Generationcited by 127
  3. 2021Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Schemecited by 99
  4. 2021RAVE: A variational autoencoder for fast and high-quality neural audio synthesiscited by 72
  5. 2023Multi-Source Diffusion Models for Simultaneous Music Generation and Separationcited by 63
  6. 2018TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfercited by 32
  7. 2018Symbolic Music Genre Transfer with CycleGANcited by 28
  8. 2018A Universal Music Translation Networkcited by 22
  9. 2021Self-Supervised VQ-VAE for One-Shot Music Style Transfercited by 13
  10. 2022The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modelingcited by 10
  11. 2024Combining audio control and style transfer using latent diffusioncited by 10
  12. 2024Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfercited by 9
  13. 2023Timbre transfer using image-to-image denoising diffusion implicit modelscited by 7
  14. 2025Prosody-Adaptable Audio Codecs for Zero-Shot Voice Conversion via In-Context Learningcited by 6
  15. 2025Discl-VC: Disentangled Discrete Tokens and In-Context Learning for Controllable Zero-Shot Voice Conversioncited by 5
  16. 2018Learning Disentangled Representations for Timber and Pitch in Music Audiocited by 4
  17. 2024DisMix: Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulationcited by 3
  18. 2025CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidancecited by 3
  19. 2024WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusioncited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Diffusion Timbre Transfer Via Mutual Information Guided Inpainting
  2. 2026Polyphonia: Zero-Shot Timbre Transfer in Polyphonic Music with Acoustic-Informed Attention Calibration ↗
  3. 2024Subtractive Training for Music Stem Insertion using Latent Diffusion Models
  4. 2025Diff-TONE: Timestep Optimization for iNstrument Editing in Text-to-Music Diffusion Models
  5. 2026Adapting Diffusion-Based Music Synthesis to Speech and Singing Voice Conversion
  6. 2026Research on Piano Timbre Transformation System Based on Diffusion Model
  7. 2026Rethinking Automatic Music Mixing as Sequential Stem Blending ↗
  8. 2025Generation of Musical Timbres using a Text-Guided Diffusion Model