  Recently, GAN-based neural vocoders such as Parallel WaveGAN, MelGAN,
HiFiGAN, and UnivNet have become popular due to their lightweight and parallel
structure, resulting in a real-time synthesized waveform with high fidelity,
even on a CPU. HiFiGAN and UnivNet are two SOTA vocoders. Despite their high
quality, there is still room for improvement. In this paper, motivated by the
structure of Vision Outlooker from computer vision, we adopt a similar idea and
propose an effective and lightweight neural vocoder called WOLONet. In this
network, we develop a novel lightweight block that uses a location-variable,
channel-independent, and depthwise dynamic convolutional kernel with
sinusoidally activated dynamic kernel weights. To demonstrate the effectiveness
and generalizability of our method, we perform an ablation study to verify our
novel design and make a subjective and objective comparison with typical
GAN-based vocoders. The results show that our WOLONet achieves the best
generation quality while requiring fewer parameters than the two neural SOTA
vocoders, HiFiGAN and UnivNet.
