MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Table 2: Mean Opinion Score of ablation studies. To evaluate the biases induced by each component, we remove them one at a time, and train the model for 500 epochs each. Evaluation protocol/details can be found in appendix B.
['Model', 'MOS', '95% CI']
First, in order to understand the importance of various components of our proposed model, we perform qualitative and quantitative analysis of the reconstructed audio for the mel-spectrogram inversion task. We remove certain key architectural decisions and evaluate the audio quality using the test set. Our analysis leads to the following conclusions: Absence of dilated convolutional stacks in the generator or removing weight normalization lead to high frequency artifacts. Using a single discriminator (instead of multi-scale discriminator) produces metallic audio, especially while the speaker is breathing. Moreover, on our internal 6 clean speakers dataset, we notice that this version of the model skips certain voiced portions, completely missing some words. Using spectral normalization or removing the window-based discriminator loss makes it harder to learn sharp high frequency patterns, causing samples to sound significantly noisy. Adding an extra L1 penalty between real and generated raw waveform makes samples sound metallic with additional high frequency artifacts.