# Microphone Conversion: Mitigating Device Variability in Sound Event Classification

arXiv: https://arxiv.org/abs/2401.06913 · HTML: https://mlsanity.com/paper/2401.06913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification](https://mlsanity.com/paper/2206.12513/) (2022)
- [Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems](https://mlsanity.com/paper/2003.12425/) (2020)
- [Low-complexity acoustic scene classification in DCASE 2022 Challenge](https://mlsanity.com/paper/2206.03835/) (2022)
- [QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design](https://mlsanity.com/paper/2206.13909/) (2022)
- [FilterAugment: An Acoustic Environmental Data Augmentation Method](https://mlsanity.com/paper/2110.03282/) (2021)
- [Heavily Augmented Sound Event Detection utilizing Weak Predictions](https://mlsanity.com/paper/2107.03649/) (2021)
- [Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification](https://mlsanity.com/paper/2104.01433/) (2021)

## Built on by (most cited first, 1 of 1)

- [Unified Microphone Conversion: Many-to-Many Device Mapping via Feature-wise Linear Modulation](https://mlsanity.com/paper/2410.18322/) (2024)

## Abstract (arXiv)

In this study, we introduce a new augmentation technique to enhance the resilience of sound event classification (SEC) systems against device variability through the use of CycleGAN. We also present a unique dataset to evaluate this method. As SEC systems become increasingly common, it is crucial that they work well with audio from diverse recording devices. Our method addresses limited device diversity in training data by enabling unpaired training to transform input spectrograms as if they are recorded on a different device. Our experiments show that our approach outperforms existing methods in generalization by 5.2% - 11.5% in weighted f1 score. Additionally, it surpasses the current methods in adaptability across diverse recording devices by achieving a 6.5% - 12.8% improvement in weighted f1 score.