WaveNet is an audio generative model based on the PixelCNN architecture. In order to deal with long-range temporal dependencies needed for raw audio generation, architectures are developed based on dilated causal convolutions, which exhibit very large receptive fields.
The joint probability of a waveform $\vec{x} = { x_1, \dots, x_T }$ is factorised as a product of conditional probabilities as follows:
$$p\left(\vec{x}\right) = \prod_{t=1}^{T} p\left(x_t \mid x_1, \dots ,x_{t-1}\right)$$
Each audio sample $x_t$ is therefore conditioned on the samples at all previous timesteps.
Source: WaveNet: A Generative Model for Raw AudioPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Speech Synthesis | 52 | 26.26% |
Text-To-Speech Synthesis | 16 | 8.08% |
Voice Conversion | 11 | 5.56% |
Audio Generation | 8 | 4.04% |
Speech Enhancement | 6 | 3.03% |
Time Series Analysis | 5 | 2.53% |
Speech Recognition | 5 | 2.53% |
Music Generation | 4 | 2.02% |
Machine Translation | 4 | 2.02% |
Component | Type |
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Temporal Convolutions | |
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Output Functions |