Axial Residual Networks for CycleGAN-based Voice Conversion

16 Feb 2021 Jaeseong You Gyuhyeon Nam Dalhyun Kim Gyeongsu Chae

We propose a novel architecture and improved training objectives for non-parallel voice conversion. Our proposed CycleGAN-based model performs a shape-preserving transformation directly on a high frequency-resolution magnitude spectrogram, converting its style (i.e. speaker identity) while preserving the speech content... (read more)

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Methods used in the Paper


METHOD TYPE
Residual Connection
Skip Connections
Batch Normalization
Normalization
Leaky ReLU
Activation Functions
Residual Block
Skip Connection Blocks
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
Cycle Consistency Loss
Loss Functions
PatchGAN
Discriminators
GAN Least Squares Loss
Loss Functions
Instance Normalization
Normalization
Convolution
Convolutions
ReLU
Activation Functions
CycleGAN
Generative Models
Axial
Image Model Blocks