wavenet:a generative Model for Raw Audio Aaron van den oord, Sander dieleman, heiga zen, Karen simonyan, oriol vinyals, Alex Graves Nal kalchbrenner, Andrew senior, Koray Kavukcuoglu (Submitted on Sep-2016 (v1), last revised Sep 2016 (this version, V2)) This is paper introduces WaveNet, a deep neural network for generating raw audio. The model is fully probabilistic and autoregressive, with the predictive distribution to each audio sample conditioned on all previous ones; Nonetheless we show, it can be efficiently trained on data with tens of thousands of the samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly mo Re natural sounding than the best parametric and concatenative systems for both 中文版 and Mandarin. A single wavenet can capture the characteristics of many different speakers with equal fidelity, and can switch between th Em by conditioning on the speaker IDentity. When trained to model music, we find that it generates novel and often highly realistic musical. We also show so it can be employed as a discriminative model, returning promising results for phoneme recognition.
| Subjects: |
Sound (CS. SD); Learning (CS. LG) |
| Cite as: |
arxiv:1609.03499 [CS. SD] |
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(or Arxiv:1609.03499v2 [CS. SD] for this version |
Submission HistoryFrom:aäron van den Oord [view email]
[V1]Mon, Sep 2016 17:29:40 GMT (3057kb,d)
[v2]Mon, Sep 2016 18:04:35 GMT (3055kb,d)
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