# ZipEnhancer: Dual-Path Down-Up Sampling-based Zipformer for Monaural Speech Enhancement

arXiv: https://arxiv.org/abs/2501.05183 · HTML: https://mlsanity.com/paper/2501.05183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [MeanFlowSE: one-step generative speech enhancement via conditional mean flow](https://mlsanity.com/paper/2509.14858/) (2025)
- [FlowSE-GRPO: Training Flow Matching Speech Enhancement via Online Reinforcement Learning](https://mlsanity.com/paper/2601.16483/) (2026)
- [Schr"odinger Bridge Mamba for One-Step Speech Enhancement](https://mlsanity.com/paper/2510.16834/) (2025)
- [Knowing When to Quit: Probabilistic Early Exits for Speech Separation](https://mlsanity.com/paper/2507.09768/) (2025)
- [Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners](https://mlsanity.com/paper/2509.16979/) (2025)

## Abstract (arXiv)

In contrast to other sequence tasks modeling hidden layer features with three axes, Dual-Path time and time-frequency domain speech enhancement models are effective and have low parameters but are computationally demanding due to their hidden layer features with four axes. We propose ZipEnhancer, which is Dual-Path Down-Up Sampling-based Zipformer for Monaural Speech Enhancement, incorporating time and frequency domain Down-Up sampling to reduce computational costs. We introduce the ZipformerBlock as the core block and propose the design of the Dual-Path DownSampleStacks that symmetrically scale down and scale up. Also, we introduce the ScaleAdam optimizer and Eden learning rate scheduler to improve the performance further. Our model achieves new state-of-the-art results on the DNS 2020 Challenge and Voicebank+DEMAND datasets, with a perceptual evaluation of speech quality (PESQ) of 3.69 and 3.63, using 2.04M parameters and 62.41G FLOPS, outperforming other methods with similar complexity levels.