Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study

TL;DR

Self-supervised convolutional audio models trained on diverse data can effectively learn flexible acoustic features, performing well across various speech and non-speech tasks with minimal domain-specific tuning.

Contribution

This study demonstrates that SSL models pre-trained on different audio domains exhibit broad transferability, often matching or surpassing domain-specific models in downstream tasks.

Findings

Pre-trained models perform well across multiple tasks.

Minimal domain-specificity advantages observed.

SSL models outperform some domain-specific baselines.

Abstract

Self-supervised learning (SSL) algorithms have emerged as powerful tools that can leverage large quantities of unlabeled audio data to pre-train robust representations that support strong performance on diverse downstream tasks. Up to now these have mostly been developed separately for speech and non-speech applications. Here, we explored the domain specificity of a convolutional model's pre-training data relative to different downstream speech and non-speech tasks using a self-supervised pre-training approach (BYOL-A). We found that these pre-trained models (regardless of whether they were pre-trained on speech data, non-speech data or both) enabled good performance on nearly all downstream tasks, beating or nearly matching the performance of popular domain-specific models. Only small domain-specificity advantages were observed between the different pre-training datasets. The popular domain-specific models used as baselines performed very well in their target domains, but generally faltered outside of them. Together, these results demonstrate that SSL methods can be a powerful way to learn flexible representations for domain specific data without labels. These models can be a powerful resource for later transfer learning, fine-tuning or data exploration applications when the downstream data are similar, but also perhaps when there may be a domain mismatch.