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Found 3 papers, 1 papers with code
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples
The basic idea is to identify a small set of human-understandable features of the classified example that are most influential on the classifier's prediction.
The Impact of Label Noise on a Music Tagger
We explore how much can be learned from noisy labels in audio music tagging.
End-to-End Adversarial White Box Attacks on Music Instrument Classification
Small adversarial perturbations of input data are able to drastically change performance of machine learning systems, thereby challenging the validity of such systems.
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