4 citations · 9 across the 8 of their papers we have counts for
9 papers
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…
Defending a Music Recommender Against Hubness-Based Adversarial Attacks
Katharina Hoedt, Arthur Flexer, Gerhard Widmer
Adversarial attacks can drastically degrade performance of recommenders and other machine learning systems, resulting in an increased demand for defence mechanisms. We present a ne…
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples
Verena Praher, Katharina Prinz, Arthur Flexer +1
Local explanation methods such as LIME have become popular in MIR as tools for generating post-hoc, model-agnostic explanations of a model's classification decisions. The basic ide…
The Impact of Label Noise on a Music Tagger
Katharina Prinz, Arthur Flexer, Gerhard Widmer
We explore how much can be learned from noisy labels in audio music tagging. Our experiments show that carefully annotated labels result in highest figures of merit, but even high…