File:Conditioning Deep Generative Raw Audio Models for Structured Automatic Music.pdf
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DescriptionConditioning Deep Generative Raw Audio Models for Structured Automatic Music.pdf |
English: 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. |
Date | |
Source | Content available at arxiv.org (Dedicated link) (archive.org) |
Author | Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis |
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current | 06:20, 11 November 2018 | 1,239 × 1,752, 8 pages (1.39 MB) | Acagastya (talk | contribs) | User created page with UploadWizard |
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Software used | LaTeX with hyperref package |
Conversion program | pdfTeX-1.40.17 |
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Page size | 595.276 x 841.89 pts (A4) |
Version of PDF format | 1.5 |
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26 June 2018
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1,453,590 byte
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