# Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

arXiv: https://arxiv.org/abs/2003.12425 · HTML: https://mlsanity.com/paper/2003.12425/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Extremal Domain Translation with Neural Optimal Transport](https://mlsanity.com/paper/2301.12874/) (2023)
- [FRuDA: Framework for Distributed Adversarial Domain Adaptation](https://mlsanity.com/paper/2112.13381/) (2021)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms](https://mlsanity.com/paper/2102.05956/) (2021)
- [An Adaptive Learning based Generative Adversarial Network for One-To-One Voice Conversion](https://mlsanity.com/paper/2104.12159/) (2021)
- [DIFFRENT: A Diffusion Model for Recording Environment Transfer of Speech](https://mlsanity.com/paper/2401.08102/) (2024)
- [From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks](https://mlsanity.com/paper/2204.07018/) (2022)
- [Indoor Smartphone SLAM with Learned Echoic Location Features](https://mlsanity.com/paper/2210.08493/) (2022)
- [MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain Speech Recognition](https://mlsanity.com/paper/2302.11224/) (2023)
- [Microphone Conversion: Mitigating Device Variability in Sound Event Classification](https://mlsanity.com/paper/2401.06913/) (2024)
- [Metacognitive Selective Ensemble for Mobile Systems](https://mlsanity.com/paper/2609.31031/) (2026)

## Abstract (arXiv)

Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world. Besides many environmental dynamics, a primary factor that impacts the robustness of audio models is microphone variability. In this work, we propose Mic2Mic -- a machine-learned system component -- which resides in the inference pipeline of audio models and at real-time reduces the variability in audio data caused by microphone-specific factors. Two key considerations for the design of Mic2Mic were: a) to decouple the problem of microphone variability from the audio task, and b) put a minimal burden on end-users to provide training data. With these in mind, we apply the principles of cycle-consistent generative adversarial networks (CycleGANs) to learn Mic2Mic using unlabeled and unpaired data collected from different microphones. Our experiments show that Mic2Mic can recover between 66% to 89% of the accuracy lost due to microphone variability for two common audio tasks.