  In this paper, we propose a novel conditional convolution network, named
location-variable convolution, to model the dependencies of the waveform
sequence. Different from the use of unified convolution kernels in WaveNet to
capture the dependencies of arbitrary waveform, the location-variable
convolution uses convolution kernels with different coefficients to perform
convolution operations on different waveform intervals, where the coefficients
of kernels is predicted according to conditioning acoustic features, such as
Mel-spectrograms. Based on location-variable convolutions, we design LVCNet for
waveform generation, and apply it in Parallel WaveGAN to design more efficient
vocoder. Experiments on the LJSpeech dataset show that our proposed model
achieves a four-fold increase in synthesis speed compared to the original
Parallel WaveGAN without any degradation in sound quality, which verifies the
effectiveness of location-variable convolutions.
