Conditioning Deep Generative Raw Audio Models for Structured Automatic Music

Abstract

Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind's WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.

arXiv commentPresented at the ISMIR 2018 Conference

References in corpus (3)

• Pixel Recurrent Neural Networks
• Visualizing and Understanding Recurrent Networks
• Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

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• GACELA -- A generative adversarial context encoder for long audio inpainting
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• The GigaMIDI Dataset with Features for Expressive Music Performance Detection