Explicit Pitch and Rhythm Factorization for Robust Music Generation Under Incomplete Context

Abstract

Abstract: Drawing an analogy with automatic image completion systems, we propose Music SketchNet, a neural network framework that allows users to specify partial musical ideas guiding automatic music generation. We focus on generating the missing measures in incomplete monophonic musical pieces, conditioned on surrounding context, and optionally guided by user-specified pitch and rhythm snippets. First, we introduce SketchVAE, a novel variational autoencoder that explicitly factorizes rhythm and pitch contour to form the basis of our proposed model. Then we introduce two discriminative architectures, Sk

Research Question

Does explicit pitch and rhythm factorization improve the robustness of music generation models against incomplete context compared to unconstrained latent diffusion approaches?

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