# LVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation

arXiv: https://arxiv.org/abs/2102.10815 · HTML: https://mlsanity.com/paper/2102.10815/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2021.

## Built on by (most cited first, 6 of 6)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis](https://mlsanity.com/paper/2204.09934/) (2022)
- [VNet: A GAN-based Multi-Tier Discriminator Network for Speech Synthesis Vocoders](https://mlsanity.com/paper/2408.06906/) (2024)
- [Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion](https://mlsanity.com/paper/2306.05708/) (2023)
- [Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN](https://mlsanity.com/paper/2103.14245/) (2021)
- [WOLONet: Wave Outlooker for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2206.09920/) (2022)

## Abstract (arXiv)

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.