[2603.25476] How Class Ontology and Data Scale Affect Audio Transfer Learning

Computer Science > Machine Learning arXiv:2603.25476 (cs) [Submitted on 26 Mar 2026] Title:How Class Ontology and Data Scale Affect Audio Transfer Learning Authors:Manuel Milling, Andreas Triantafyllopoulos, Alexander Gebhard, Simon Rampp, Björn W. Schuller View a PDF of the paper titled How Class Ontology and Data Scale Affect Audio Transfer Learning, by Manuel Milling and 4 other authors View PDF HTML (experimental) Abstract:Transfer learning is a crucial concept within deep learning that allows artificial neural networks to benefit from a large pre-training data basis when confronted with a task of limited data. Despite its ubiquitous use and clear benefits, there are still many open questions regarding the inner workings of transfer learning and, in particular, regarding the understanding of when and how well it works. To that extent, we perform a rigorous study focusing on audio-to-audio transfer learning, in which we pre-train various model states on (ontology-based) subsets of AudioSet and fine-tune them on three computer audition tasks, namely acoustic scene recognition, bird activity recognition, and speech command recognition. We report that increasing the number of samples and classes in the pre-training data both have a positive impact on transfer learning. This is, however, generally surpassed by similarity between pre-training and the downstream task, which can lead the model to learn comparable features. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603...