# Conditional Generative Data Augmentation for Clinical Audio Datasets

arXiv: https://arxiv.org/abs/2203.11570 · HTML: https://mlsanity.com/paper/2203.11570/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)

## Built on by (most cited first, 5 of 5)

- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [Surgical Workflow Recognition and Blocking Effectiveness Detection in Laparoscopic Liver Resections with Pringle Maneuver](https://mlsanity.com/paper/2408.10538/) (2024)
- [Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data](https://mlsanity.com/paper/2210.13958/) (2022)
- [Improved Techniques for the Conditional Generative Augmentation of Clinical Audio Data](https://mlsanity.com/paper/2211.02874/) (2022)
- [Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes](https://mlsanity.com/paper/2510.24332/) (2025)

## Abstract (arXiv)

In this work, we propose a novel data augmentation method for clinical audio datasets based on a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our method, we created a clinical audio dataset which was recorded in a real-world operating room during Total Hip Arthroplasty (THA) procedures and contains typical sounds which resemble the different phases of the intervention. We demonstrate the capability of the proposed method to generate realistic class-conditioned samples from the dataset distribution and show that training with the generated augmented samples outperforms classical audio augmentation methods in terms of classification performance. The performance was evaluated using a ResNet-18 classifier which shows a mean Macro F1-score improvement of 1.70% in a 5-fold cross validation experiment using the proposed augmentation method. Because clinical data is often expensive to acquire, the development of realistic and high-quality data augmentation methods is crucial to improve the robustness and generalization capabilities of learning-based algorithms which is especially important for safety-critical medical applications. Therefore, the proposed data augmentation method is an important step towards improving the data bottleneck for clinical audio-based machine learning systems.