Groove2Groove One Shot Music Style Transfer with Supervision from Synthetic Data

Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match a given example . The task has interesting practical applications within the music industry, but it has so far received little attention from the audio and music processing community… In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz . We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples . This synthetic parallel data allows us to tackle the style transfer problem using end-to-end supervised learning, employing powerful techniques used in natural language processing .

Links: PDF - Abstract

Code :

https://github.com/cifkao/groove2groove

Keywords : music - style - transfer - groove - data -

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