Adversarial Audio Synthesis

Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation... (read more)

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Methods used in the Paper


METHOD TYPE
Batch Normalization
Normalization
ReLU
Activation Functions
Tanh Activation
Activation Functions
DCGAN
Generative Models
Dense Connections
Feedforward Networks
Convolution
Convolutions
Leaky ReLU
Activation Functions
Dropout
Regularization
Griffin-Lim Algorithm
Phase Reconstruction
WGAN-GP Loss
Loss Functions
SpecGAN
Generative Audio Models
Adam
Stochastic Optimization
Phase Shuffle
Audio Artifact Removal
WaveGAN
Generative Audio Models