4 citations · 9 across the 8 of their papers we have counts for
3 papers · 1 filter
Are Inherently Interpretable Models More Robust? A Study In Music Emotion Recognition
Katharina Hoedt, Arthur Flexer, Gerhard Widmer
One of the desired key properties of deep learning models is the ability to generalise to unseen samples. When provided with new samples that are (perceptually) similar to one or m…
Validity in Music Information Research Experiments
Bob L. T. Sturm, Arthur Flexer
Validity is the truth of an inference made from evidence, such as data collected in an experiment, and is central to working scientifically. Given the maturity of the domain of mus…
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
Francesco Foscarin, Katharina Hoedt, Verena Praher +2
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-freque…