# MicAugment: One-shot Microphone Style Transfer

arXiv: https://arxiv.org/abs/2010.09658 · HTML: https://mlsanity.com/paper/2010.09658/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [An Overview of Indian Spoken Language Recognition from Machine Learning Perspective](https://mlsanity.com/paper/2212.03812/) (2022)
- [Music Augmentation and Denoising For Peak-Based Audio Fingerprinting](https://mlsanity.com/paper/2310.13388/) (2023)
- [Device-Guided Music Transfer](https://mlsanity.com/paper/2511.17136/) (2025)

## Abstract (arXiv)

A crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions. In this work, we propose a method to perform one-shot microphone style transfer. Given only a few seconds of audio recorded by a target device, MicAugment identifies the transformations associated to the input acquisition pipeline and uses the learned transformations to synthesize audio as if it were recorded under the same conditions as the target audio. We show that our method can successfully apply the style transfer to real audio and that it significantly increases model robustness when used as data augmentation in the downstream tasks.