Papers for

audio machine learning engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Procedural audio pre training improves with source scaling and mask tuning

Rethinking Procedural Audio Pre-training: Source Scaling and Objective Adaptation

Abstract: Procedural audio has emerged as a viable source for transferable audio representation learning, but its design principles remain unclear.We revisit two questions: how a procedural source should be scaled, and whether training choices developed on natural audio should transfer unchanged to procedural data.Using a controlled source, we separate scale into formula-class coverage C and within-class rendering diversity I.Experiments with FDSL and AudioMAE show that these two forms of scale provide different benefits and depend on the learning formulation and downstream task. A matched AudioMAE study further shows that procedural audio favors low mask ratios (10%--25%), whereas AudioSet-28K favors 50%--75%. Shared-codebook analysis reveals lower patch diversity and stronger temporal predictability in procedural audio. These results motivate source-aware procedural pre-training, where source scaling and learning configuration are considered jointly.Code is available at https://github.com/Cross-Innovation-Lab/Formula-Bank.

Mon 14 SeptSoundArtificial Intelligence

The gist

Procedural audio, which is artificially generated sound, can help teach machines to understand audio better. The authors studied how to best create and use these sounds to teach machines by looking at two things: how varied the sound formulas are and how much variety there is in the sounds they produce. They found that different ways of making these sounds help in different learning tasks, and that settings used for regular audio don't always work well with procedural audio. Their work suggests adjusting training methods depending on the kind of audio source being used.

Open → 2609.15067v1