Micaugment: One-Shot Microphone Style Transfer
- Resource Type
- Conference
- Authors
- Borsos, Zalan; Li, Yunpeng; Gfeller, Beat; Tagliasacchi, Marco
- Source
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021 - 2021 IEEE International Conference on. :3400-3404 Jun, 2021
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Performance evaluation
Conferences
Pipelines
Signal processing
Robustness
Data models
Object recognition
style transfer
robustness
data augmentation
- Language
- ISSN
- 2379-190X
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 with 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.