Modulated Variational Auto-Encoders for Many-to-Many Musical Timbre Transfer

1 INTRODUCTION . Music information can be analyzed in many forms , each of which conveys different specificities over musical qualities . Among these , timbre is the set of properties that distinguishes one instrument from another playing at the same pitch and loudness . Timbre has become a core concept in music composition since the 19th century ( McAdams ( 2013 ) ) . It has been studied using human dissimilarity ratings to construct timbre spaces , which exhibit the perceptual relationships between instruments ( Grey ( 1977 ) ) . However , these spaces are not invertible to the signal domain and do not generalize to new examples ( McAdams et al . ( 2006 ) ) . The heavy reliance on hand-crafted audio descriptors to analyze timbre perception altogether leads to a lack of established models to understand and generate timbres ( McAdams ( 2013 ) ) . Moreover , the specific nature of music tasks requires tailored evaluation principles that are yet to be ascertained ( Jaffe ( 1995 ) ) . Recent advances in generative models open alternative avenues to analyze highly dimensional data and tackle complex subsequent tasks . Amongst these , the idea of style transfer ( Gatys et al . ( 2015 ) ) has recently gained a flourishing interest . This approach allows to modify the stylistic features of an image while preserving its overall content and led to the more generic question of domain translation . In the recent UNsupervised Image-to-image Translation ( UNIT ) model , Liu et al . ( 2017 ) proposed to learn a shared latent space with a Variational Auto-Encoder ( VAE ) and translate between different data domains with an adversarial criterion . However , specific properties of the generation can not be controlled and that discriminative objective might lead to an unstable and longer training . Here , we first extend this approach to musical transfer while improving it by introducing Modulated Variational auto-Encoders ( MoVE ) that offer control over the generation through conditioning . 1 Audio examples , source code and animations available at https : //github.com/anonymous124/iclr2019MoVE Furthermore , by replacing the discriminative networks by a Maximum Mean Discrepancy ( MMD ) objective , we alleviate the need for an additional adversarial training specific to each domain . Although UNIT provides a powerful framework , it only applies to one-to-one transfer . This implies that a different model has to be trained for each pair of domains . To mitigate this issue , Choi et al . ( 2017 ) proposed StarGAN which performs many-to-many transfer between several domains . However , it relies solely on Generative Adversarial Networks ( GANs ) and do not learns an implicit task representation to interact with . In the music realm , Mor et al . ( 2018 ) proposed Universal Music Translation ( UMT ) , which does not use GANs . Although it enables translation across multiple complex audio domains , this method requires to learn a separate decoder for each domain , which leads to a prohibitive training time . In contrast to these methods , we show that MoVE can be further conditioned on domain information and generalizes to many-to-many transfer with a single encoder and decoder architecture able to perform multi-domain transfer . The resulting models are rather lightweight and fast to train while effective on a moderate amount of examples . Here , we define timbre transfer as applying a variable part of the auditory properties of a musical instrument onto another . We circumvent the lack of definition for timbre by considering each instrument as a separate domain that maps onto a common latent representation . We further address the crucial need for interactivity and control in creative applications such as audio synthesis . Accordingly , our method yields 3-dimensional latent spaces that can be explored and controlled through high-level explicit variables such as pitch and octave values . This supports sound generation with smoothly evolving timbre qualities and complex domain transfers from a reduced set of parameters . Finally , we analyze traditional audio descriptor distributions when transferring between multiple domains or decoding across latent dimensions to demonstrate the generative capacities of our model . 2 RELATED WORKS . Style transfer and image translation . In computer vision , style transfer ( Gatys et al . ( 2015 ) ) has been proposed to generate images that preserve the content from a source image but feature stylistic qualities belonging to another target image . Although this technique provides compelling results , it operates on local textural information and fails to capture higher-level semantic properties of the style . Further research has been carried to address this question of domain translation , first proposed by Isola et al . ( 2017 ) . In the fully supervised setting , this translation would require paired samples . However , such datasets are scarce and the concept of existing samples exactly matching the translation task is restrictive from a generative perspective . In the UNIT approach ( Liu et al . ( 2017 ) ) , the underlying assumption is that two hypothetically matching samples should map onto the same point in a shared latent space . Hence , translation is achieved by partially weight-shared VAEs in order to map the two separate domains to a common latent representation . Learning is performed with an auxiliary pair of adversarial discriminators which push translated samples to match the distributions of their respective domains . An additional cycle-consistency objective ( Zhu et al . ( 2017 ) ) reinforces the shared learning by ensuring that translated samples can be retrieved back to their original domains . However , this architecture can only operate on single domain pairs . In order to provide many-to-many translations , Choi et al . ( 2017 ) proposed to replace weight-sharing by conditioning a single GAN . This allows to train on multiple domains simultaneously , while enabling control over the generative process . However , the authors evaluate only on highly similar domains ( eg . face attributes ) . Furthermore , this approach relies solely on GANs , which are notoriously difficult to train , prone to lack full support over the data ( Grover et al . ( 2017 ) ) and do not provide a latent space encoder . Recently , Feature-wise Linear Modulation ( FiLM ) has been proposed to improve conditioning by learning conditional bias and scaling throughout a network ( Perez et al . ( 2017 ) ) . This method was successfully used in image stylization ( Ghiasi et al . ( 2017 ) ) where adaptive modulation conditioned on a style image is applied after each intermediate instance normalization . Here , we show that by relying on FiLM layers for domain conditioning , we can perform many-to-many domain translation with a single VAE architecture , as depicted in Fig . 1 ( c ) . Moreover , by using a MMD criterion , we alleviate the need for GANs or specific adversarial discriminators . Hence , we obtain an unsupervised , lightweight and easy to train model with a general and controllable latent space . 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( a ) One-to-one transfer models such as UNIT are restricted to domain pairs . ( b ) One-to-many transfer models such as UMT require to train a different decoder for each domain . ( c ) Our proposed many-to-many transfer model ( MoVE ) allows to perform multi-domain transfer with a single encoder and decoder , while providing control over the generation with external high-level conditioning variables . Audio translations . Recent applications of generative models to audio have shown promising results , notably supported by solutions to efficiently generate waveforms such as Wavenet ( Van Den Oord et al . ( 2016 ) ) or SampleRNN ( Mehri et al . ( 2017 ) ) . Most of these proposals target voice signals and there is still a large gap when addressing musical data . Some approaches have tackled musical style transfer ( Verma & Smith ( 2018 ) ) . However , as pointed by Dai et al . ( 2018 ) , musical style is a multimodal and multi-scale notion , which implies a variety of underlying factors . Specifically for domain translation , Mor et al . ( 2018 ) proposed a Universal Music Translation ( UMT ) network that globally translates musical recordings between different genres and instrument domains . Using a single Wavenet encoder and separate decoders for each domain , this approach is able to transform a given melody so that it is played by different instruments . By design , this method requires to train a specific Wavenet decoder for each of the target domain . It does not provide control over audio synthesis and the learned representation does not allow direct visualization nor transfer of only specific parts of timbre attributes . Hence , it does not enable informed generative processes , musical interaction and creativity . Our proposal targets 3-dimensional latent spaces supporting timbre transfer and continuous synthesis paths with explicit control over musical attributes .

Summary: The authors proposed a Modulated Variational auto-Encoders (MoVE) to perform musical timbre transfer. The authors define timbre transfer as applying parts of the auditory properties of a musical instrument onto another. It replaces the usual adversarial translation criterion by a Maximum Mean Discrepancy (MMD) objective. By further conditioning our system on several different instruments, the proposed method can generalize to many-to-many transfer within a single variational architecture able to perform multi-domain transfers.