# TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network

arXiv: https://arxiv.org/abs/2505.18533 · HTML: https://mlsanity.com/paper/2505.18533/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 18 of 18)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [Universal Speech Enhancement with Score-based Diffusion](https://mlsanity.com/paper/2206.03065/) (2022)
- [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022)
- [Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation](https://mlsanity.com/paper/2109.05418/) (2021)
- [SpeechBERTScore: Reference-Aware Automatic Evaluation of Speech Generation Leveraging NLP Evaluation Metrics](https://mlsanity.com/paper/2401.16812/) (2024)
- [VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration](https://mlsanity.com/paper/2204.05841/) (2022)
- [ICASSP 2024 Speech Signal Improvement Challenge](https://mlsanity.com/paper/2401.14444/) (2024)
- [Towards High-Quality and Efficient Speech Bandwidth Extension with Parallel Amplitude and Phase Prediction](https://mlsanity.com/paper/2401.06387/) (2024)
- [Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music](https://mlsanity.com/paper/2008.05216/) (2020)
- [Audio Codec Enhancement with Generative Adversarial Networks](https://mlsanity.com/paper/2001.09653/) (2020)
- [Toward Universal Speech Enhancement for Diverse Input Conditions](https://mlsanity.com/paper/2309.17384/) (2023)
- [ICASSP 2023 Speech Signal Improvement Challenge](https://mlsanity.com/paper/2303.06566/) (2023)
- [KS-Net: Multi-band joint speech restoration and enhancement network for 2024 ICASSP SSI Challenge](https://mlsanity.com/paper/2402.01808/) (2024)
- [BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators](https://mlsanity.com/paper/2401.03687/) (2024)
- [RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement](https://mlsanity.com/paper/2401.04389/) (2024)

## Built on by (most cited first, 2 of 2)

- [GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement](https://mlsanity.com/paper/2604.01832/) (2026)
- [UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations](https://mlsanity.com/paper/2604.14606/) (2026)

## Abstract (arXiv)

Universal speech enhancement aims to handle input speech with different distortions and input formats. To tackle this challenge, we present TS-URGENet, a Three-Stage Universal, Robust, and Generalizable speech Enhancement Network. To address various distortions, the proposed system employs a novel three-stage architecture consisting of a filling stage, a separation stage, and a restoration stage. The filling stage mitigates packet loss by preliminarily filling lost regions under noise interference, ensuring signal continuity. The separation stage suppresses noise, reverberation, and clipping distortion to improve speech clarity. Finally, the restoration stage compensates for bandwidth limitation, codec artifacts, and residual packet loss distortion, refining the overall speech quality. Our proposed TS-URGENet achieved outstanding performance in the Interspeech 2025 URGENT Challenge, ranking 2nd in Track 1.