Audio generation has long been an area of interest in the field of deep learning. The MelGAN Residual Block is a convolutional residual block used in the MelGAN generative audio architecture, aimed to generate high-quality audio waveforms from mel-spectrogram input at high sampling rates.
What is a Residual Block?
A residual block is a shortcut connection from input to output, designed to overcome the issues of gradient vanishing or exploding. The residual connections provide an alternative and shorter path for gradients to propagate. The residual block consists of a series of convolutional layers that produce activations, implementing the non-linerities and pattern extraction.
What is the MelGAN Residual Block?
The MelGAN Residual Block is an improvement on the traditional residual block, incorporating dilated convolutions. The dilation parameter adds spacing between the kernel points(1 adds no spacing). This parameter allows us to expand the receptive fields exponentially without increasing the number of layers. The MelGAN Residual Block employs residual connections with dilated convolutions. This way, we guarantee a strong segment from the input to the output of the convolutions, thus improving the overall performance.
How it Works?
The MelGAN Residual Block works by increasing the receptive fields of each output time-step. A stack of dilated convolutional layers increases the receptive field exponentially with the number of layers. So the MelGAN generator efficiently increases the induced receptive fields while providing larger overlap in the induced receptive field of far apart time-steps. This results in better long-range correlation between the input and output time-steps which improves the performance. The high-quality audio waveforms generated by the MelGAN Residual Block demonstrate its superiority in generative audio settings.
In summary, the MelGAN Residual Block is used in the architecture of MelGAN generative audio to produce high-quality audio waveforms. The use of dilated convolutions with residual connections enables the efficient increase of induced receptive fields while providing significant overlapping inputs. This leads to better long-range correlation, which improves the overall quality of generative audio.
