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Found 3 papers, 2 papers with code
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e. g., by highlighting potentially relevant time-frequency bins in a spectrogram or time-pitch bins in a piano roll.
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.
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities
In previous work, we have shown how to derive explanations of model predictions in terms of spectrogram image segments that connect to the high-level emotion prediction via a layer of easily interpretable perceptual features.
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