2018a)) to sequences, creating large volumes of paired data by performing simple transformations and training generative models to plausibly invert these transformations. Pix2Pix (Isola et al., 2017) and Vid2Vid (Wang et al. Though Seq2Seq models usually require painstakingly aligned corpora, we show that it is possible to adapt an approach from the Generative Adversarial Network (GAN) literature (e.g. We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models. Additionally, we discuss the benefit of incorporating control elements - genre, velocity and microtiming patterns - into the model. We show that our two-step approach and the usage of a discrete encoding space improves the learning of the original data distribution.
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In order to model the drum notes, we apply a discrete latent representation method via Vector Quantized Variational Autoencoder (VQ-VAE), as drum notes have a discrete property, unlike velocity and microtiming values. First, the note module updates the user template through addition and deletion of notes Second, the velocity and microtiming modules add details to this generated note score.
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Our system, consisting of different modules for each groove component, takes a two-step approach that is analogous to a music creation process. Grooves can be either transferred from a reference track, generated randomly or with conditions, such as genres. In this work, we introduce PocketVAE, a groove generation system that applies grooves to users' rudimentary MIDI tracks, i.e, templates. Creating a good drum track to imitate a skilled performer in digital audio workstations (DAWs) can be a time-consuming process, especially for those unfamiliar with drums.