# Complex Recurrent Variational Autoencoder with Application to Speech Enhancement

arXiv: https://arxiv.org/abs/2204.02195 · HTML: https://mlsanity.com/paper/2204.02195/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2022.

## Built on by (most cited first, 1 of 1)

- [Complex variational autoencoders admit K"ahler structure](https://mlsanity.com/paper/2511.15172/) (2025)

## Abstract (arXiv)

As an extension of variational autoencoder (VAE), complex VAE uses complex Gaussian distributions to model latent variables and data. This work proposes a complex recurrent VAE framework, specifically in which complex-valued recurrent neural network and L1 reconstruction loss are used. Firstly, to account for the temporal property of speech signals, this work introduces complex-valued recurrent neural network in the complex VAE framework. Besides, L1 loss is used as the reconstruction loss in this framework. To exemplify the use of the complex generative model in speech processing, we choose speech enhancement as the specific application in this paper. Experiments are based on the TIMIT dataset. The results show that the proposed method offers improvements on objective metrics in speech intelligibility and signal quality.