  Recent neural vocoders usually use a WaveNet-like network to capture the
long-term dependencies of the waveform, but a large number of parameters are
required to obtain good modeling capabilities. In this paper, an efficient
network, named location-variable convolution, is proposed to model the
dependencies of waveforms. Different from the use of unified convolution
kernels in WaveNet to capture the dependencies of arbitrary waveforms,
location-variable convolutions utilizes a kernel predictor to generate multiple
sets of convolution kernels based on the mel-spectrum, where each set of
convolution kernels is used to perform convolution operations on the associated
waveform intervals. Combining WaveGlow and location-variable convolutions, an
efficient vocoder, named MelGlow, is designed. Experiments on the LJSpeech
dataset show that MelGlow achieves better performance than WaveGlow at small
model sizes, which verifies the effectiveness and potential optimization space
of location-variable convolutions.
