# Hybrid Real- and Complex-Valued Neural Network Architecture for Speech Enhancement

arXiv: https://arxiv.org/abs/2509.21185 · HTML: https://mlsanity.com/paper/2509.21185/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 12 of 12)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain](https://mlsanity.com/paper/2203.17004/) (2022)
- [PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network](https://mlsanity.com/paper/1911.04697/) (2019)
- [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (2022)
- [On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement](https://mlsanity.com/paper/1909.01019/) (2019)
- [Rethinking complex-valued deep neural networks for monaural speech enhancement](https://mlsanity.com/paper/2301.04320/) (2023)
- [A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)](https://mlsanity.com/paper/2207.14799/) (2022)
- [Spectral Masking with Explicit Time-Context Windowing for Neural Network-Based Monaural Speech Enhancement](https://mlsanity.com/paper/2408.15582/) (2024)
- [Hybrid Real- and Complex-valued Neural Network Architecture](https://mlsanity.com/paper/2504.03497/) (2025)

## Abstract (arXiv)

This paper investigates hybrid real- and complex-valued neural networks for monaural speech enhancement. While complex-valued models can process time-frequency representations natively, they often increase computational cost and can be inefficient in small-model regimes. We therefore study a matched-parameter hybrid architecture that combines a real-valued magnitude-mask branch with a complex-valued additive correction branch, coupled via domain conversion functions at the bottleneck. The approach is applied to convolutional denoising autoencoder and convolutional-recurrent network architectures. Averaged over four SNRs, the hybrid models improve intelligibility and quality over the considered counterparts, while substantially reducing the number of operations.