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TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

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arxiv 1811.09620 v3 pith:6SLQO5TB submitted 2018-11-22 cs.SD cs.LGeess.ASstat.ML

TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

Sicong Huang , Qiyang Li , Cem Anil , Xuchan Bao , Sageev Oore , Roger B. Grosse This is my paper
classification cs.SD cs.LGeess.ASstat.ML
keywords timbremusicaltransferrepresentationaudiotimbretroncontenthigh-quality
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies "image" domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    cs.SD 2026-05 unverdicted novelty 7.0

    MixtureTT performs direct per-stem timbre transfer on polyphonic mixtures via a shared diffusion transformer, outperforming single-stem baselines on SATB choral data while eliminating cascaded separation errors.

  2. Latent Fourier Transform

    cs.SD 2026-04 unverdicted novelty 7.0

    LatentFT uses latent-space Fourier transforms and frequency masking in diffusion autoencoders to enable timescale-specific manipulation of musical structure in generative models.

  3. Repurposing Image Diffusion Models for Training-Free Music Style Transfer on Mel-spectrograms

    cs.SD 2024-11 conditional novelty 7.0

    Stylus achieves training-free music style transfer on Mel-spectrograms by repurposing image diffusion models via style-key injection in self-attention plus phase-preserving reconstruction, outperforming baselines by 3...

  4. Music Transcription with (Almost) No Supervision

    cs.SD 2026-05 unverdicted novelty 5.0

    Cycle-consistent translation enables competitive music transcription performance with mostly unpaired audio and scores plus minimal paired supervision.