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Found 7 papers, 3 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.
Defending a Music Recommender Against Hubness-Based Adversarial Attacks
Adversarial attacks can drastically degrade performance of recommenders and other machine learning systems, resulting in an increased demand for defence mechanisms.
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.
scikit-hubness: Hubness Reduction and Approximate Neighbor Search
Users will find all functionality of the scikit-learn neighbors package, plus additional support for transparent hubness reduction and approximate nearest neighbor search.
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?
The theoretical results show that approximately reproducing mel-spectrogram coefficients by applying adaptive filters and subsequent time-averaging is in principle possible.
