In this paper, we propose hybrid real- and complex-valued neural networks for speech enhancement. Real- or complex-valued models are either inefficient or present high complexity. We devise a straightforward design method for extending a real-valued network into its hybrid counterpart. Based on speech intelligibility and quality metrics, we compare the real, complex, and hybrid versions of a convolutional and a convolutional-recurrent architecture. The hybrid network consistently outperforms its counterparts with the same number of parameters. Additionally, the hybrid models' complexity in terms of multiply-accumulate operations is substantially lower than that of their counterparts.
@article{arxiv.2509.21185,
title = {Hybrid Real- And Complex-Valued Neural Network Concept For Low-Complexity Phase-Aware Speech Enhancement},
author = {Luan Vinícius Fiorio and Alex Young and Ronald M. Aarts},
journal= {arXiv preprint arXiv:2509.21185},
year = {2025}
}