arXiv (Cornell University)· 2016· Preprint
WaveNet: A Generative Model for Raw Audio
- 2,472citations
- 2016year
Short summary
WaveNet is a novel deep neural network that generates raw audio waveforms by predicting each audio sample based on all previous ones, achieving state-of-the-art naturalness in text-to-speech synthesis.
AI-generated from the title and abstract; the full text is not read.
Key points
- WaveNet is a deep neural network for generating raw audio waveforms.
- It uses a fully probabilistic and autoregressive model, conditioning each sample on all prior samples.
- Achieves state-of-the-art naturalness in text-to-speech for English and Mandarin.
- Can model multiple speakers and generate realistic musical fragments.
- Shows promise as a discriminative model for phoneme recognition.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of 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 more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.
The authors' abstract, as published at the source. arXiv (Cornell University), 2016 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science