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

Paper content:
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.
We made an accompanying demo video here: https://www.cs.toronto.edu/~huang/TimbreTron/index.html which we strongly encourage you to watch before reading the paper.
Timbre is a perceptual characteristic that distinguishes one musical instrument from another playing the same note with the same intensity and duration.
Modeling timbre is very hard, and it has been referred to as "the psychoacoustician's multidimensional waste-basket category for everything that cannot be labeled pitch or loudness" 2 .
The timbre of a single note at a single pitch has a nonlinear dependence on the volume, time and even the particular way the instrument is played by the performer.
While there is a substantial body of research in timbre modelling and synthesis BID6 ; BID32 ; BID35 BID36 ), state-of-the-art musical sound libraries used by orchestral composers for analog instruments (e.g. the Vienna Symphonic Library (GmbH, 2018) ) are still obtained by extremely careful audio sampling of real instrument recordings.
Being able to model and manipulate timbre electronically carries importance for musicians who wish to experiment with different sounds, or compose for multiple instruments.
(Appendix A discusses the components of music in more detail.)In this paper, we consider the problem of high quality timbre transfer between audio clips obtained with different instruments.
More specifically, the goal is to transform the timbre of a musical recording to match a set of reference recordings while preserving other musical content, such as pitch and loudness.
We take inspiration from recent successes in style transfer for images using neural networks BID13 BID22 BID7 ).
An appealing strategy would be to directly apply image-based style transfer techniques to time-frequency representations of images, such as short-time Fourier transform (STFT) spectrograms.
However, needing to convert the generated spectrogram into a waveform presents a fundamental obstacle, since accurate reconstruction requires phase information, which is difficult to predict BID10 , and existing techniques for inferring phase (e.g., BID16 ) can produce characteristic artifacts which are undesirable for high quality audio generation BID34 .Recent
years have seen rapid progress on audio generation methods that directly generate high-quality waveforms, such as WaveNet BID41 , SampleRNN BID28 , and Tacotron2 BID34 ). WaveNet
's ability to condition on abstract audio representations is particularly relevant, since it enables one to perform manipulations in high-level auditory representations from which reconstruction would have previously been impractical. Tacotron2
performs high-level processing on time-frequency representations of speech, and then uses WaveNet to output high-quality audio conditioned on the generated mel spectrogram.We adapt this general strategy to the music domain. We propose
TimbreTron, a pipeline that performs CQT-based timbre transfer with high-quality waveform output. It is trained
only on unrelated samples of two instruments. For our time-frequency
representation, we choose the constant Q transform (CQT), a perceptually motivated representation of music BID4 . We show that this representation
is particularly well-suited to musical timbre transfer and other manipulations due to its pitch equivariance and the way it simultaneously achieves high frequency resolution at low frequencies and high temporal resolution at high frequencies, a property that STFT lacks.TimbreTron performs timbre transfer by three steps, shown in Figure 1 . First, it computes the CQT spectrogram
and treats its log-magnitude values as an image (discarding phase information). Second, it performs timbre transfer in
the log-CQT domain using a CycleGAN (Zhu et al., 2017) . Finally, it converts the generated log-CQT
to a waveform using a conditional WaveNet synthesizer (which implicitly must infer the missing phase information). Empirically, our TimbreTron can successfully
perform musical timbre transfer on some instrument pairs. The generated audio samples have realistic timbre
that matches the target timbre while otherwise expressing the same musical content (e.g., rhythm, loudness, pitch). We empirically verified that the use of a CQT representation
is a crucial component in TimbreTron as it consistently yields qualitatively better timbre transfer than its STFT counterpart.
We presented the TimbreTron, a pipeline for perfoming high-quality timbre transfer on musical waveforms using CQT-domain style transfer.
We perform the timbre transfer in the time-frequency domain, and then reconstruct the inputs using a WaveNet (circumventing the difficulty of phase recovery from an amplitude CQT).
The CQT is particularly well suited to convolutional architectures due to its approximate pitch equivariance.
The entire pipeline can be trained on unrelated real-world music segments, and intriguingly, the MIDI-trained CycleGAN demonstrated generalization capability to real-world musical signals.
Based on an AMT study, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and poly-

assistant: text: The authors use multiple techniques/tools to enable neural timbre transfer (converting music from one instrument to another) without paired training examples.