# Unsupervised Noise adaptation using Data Simulation

arXiv: https://arxiv.org/abs/2302.11981 · HTML: https://mlsanity.com/paper/2302.11981/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1711.10288/) (2017)
- [Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data](https://mlsanity.com/paper/2203.15321/) (2022)
- [Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning](https://mlsanity.com/paper/2210.15324/) (2022)
- [Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning](https://mlsanity.com/paper/2212.05301/) (2022)
- [Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition](https://mlsanity.com/paper/2110.05267/) (2021)
- [Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport](https://mlsanity.com/paper/2111.06316/) (2021)
- [Noise Adaptive Speech Enhancement using Domain Adversarial Training](https://mlsanity.com/paper/1807.07501/) (2018)
- [Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning](https://mlsanity.com/paper/2203.15526/) (2022)
- [Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition](https://mlsanity.com/paper/2203.14838/) (2022)
- [Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation](https://mlsanity.com/paper/2302.11131/) (2023)
- [Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition](https://mlsanity.com/paper/2302.11362/) (2023)
- [Self-critical Sequence Training for Automatic Speech Recognition](https://mlsanity.com/paper/2204.06260/) (2022)

## Built on by (most cited first, 9 of 9)

- [Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models](https://mlsanity.com/paper/2405.10025/) (2024)
- [Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR](https://mlsanity.com/paper/2304.04974/) (2023)
- [Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2305.09212/) (2023)
- [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023)
- [MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10567/) (2023)
- [Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech Recognition](https://mlsanity.com/paper/2409.12386/) (2024)
- [Effective Noise-aware Data Simulation for Domain-adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation](https://mlsanity.com/paper/2409.01545/) (2024)
- [Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge](https://mlsanity.com/paper/2402.01413/) (2024)
- [Universal Robust Speech Adaptation for Cross-Domain Speech Recognition and Enhancement](https://mlsanity.com/paper/2602.04307/) (2026)

## Abstract (arXiv)

Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm. However, such a trained-well transformation is vulnerable to unseen noises that are not included in training set. In this work, we focus on the unsupervised noise adaptation problem in speech enhancement, where the ground truth of target domain data is completely unavailable. Specifically, we propose a generative adversarial network based method to efficiently learn a converse clean-to-noisy transformation using a few minutes of unpaired target domain data. Then this transformation is utilized to generate sufficient simulated data for domain adaptation of the enhancement model. Experimental results show that our method effectively mitigates the domain mismatch between training and test sets, and surpasses the best baseline by a large margin.