1 citations · 2 across the 11 of their papers we have counts for
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What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings
Mateusz Modrzejewski, Tomasz Radzikowski
Open audio-language models are used as judges of generated music: given a clip and its text prompt, the model rates the clip, and the judge is validated by how well its ratings cor…
MI-MIDI: Mechanistic Interpretability of Text-to-MIDI Generation Models via Probing, Lenses and Steering
Jakub Poćwiardowski, Mateusz Modrzejewski
Mechanistic interpretability of music generation has concentrated on audio models, leaving symbolic models largely unexplored. We analyze two public text-to-MIDI systems of contras…
The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions
Piotr Kitłowski, Dominik Wiącek, Mateusz Modrzejewski
This paper investigates the fragility of post-hoc explanation methods in audio deepfake detection. While previous work on explanation manipulation focused on images using standard…
Deep Dreams Are Made of This: Visualizing Monosemantic Features in Diffusion Models
Adam Szokalski, Mateusz Modrzejewski
This paper proposes latent visualization by optimization (LVO), a mechanistic interpretability technique that extends feature visualization by optimization - originally developed f…
ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models
Bruno Sienkiewicz, Łukasz Neumann, Mateusz Modrzejewski
Concept-based interpretability methods like TCAV require clean, well-separated positive and negative examples for each concept. Existing music datasets lack this structure: tags ar…
TADA! Tuning Audio Diffusion Models through Activation Steering
Łukasz Staniszewski, Katarzyna Zaleska, Mateusz Modrzejewski +1
Audio diffusion models can synthesize high-fidelity music from text, yet achieving fine-grained control over specific musical attributes remains challenging, as their internal mech…