1 citations · 2 across the 11 of their papers we have counts for
12 papers
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…
Evaluating Fake Music Detection Performance Under Audio Augmentations
Tomasz Sroka, Tomasz Wężowicz, Dominik Sidorczuk +1
With the rapid advancement of generative audio models, distinguishing between human-composed and generated music is becoming increasingly challenging. As a response, models for det…
Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation
Jan Retkowski, Jakub Stępniak, Mateusz Modrzejewski
In this paper we introduce the Frechet Music Distance (FMD), a novel evaluation metric for generative symbolic music models, inspired by the Frechet Inception Distance (FID) in com…
MidiTok Visualizer: a tool for visualization and analysis of tokenized MIDI symbolic music
Michał Wiszenko, Kacper Stefański, Piotr Malesa +2
Symbolic music research plays a crucial role in music-related machine learning, but MIDI data can be complex for those without musical expertise. To address this issue, we present…
Symbotunes: unified hub for symbolic music generative models
Paweł Skierś, Maksymilian Łazarski, Michał Kopeć +1
Implementations of popular symbolic music generative models often differ significantly in terms of the libraries utilized and overall project structure. Therefore, directly compari…
CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music
Aleksandra Piekarzewicz, Tomasz Sroka, Aleksander Tym +1
In this paper, we introduce CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts. We describe the selected qualities of our dataset…
M6(GPT)3: Generating Multitrack Modifiable Multi-Minute MIDI Music from Text using Genetic algorithms, Probabilistic methods and GPT Models in any Progression and Time Signature
Jakub Poćwiardowski, Mateusz Modrzejewski, Marek S. Tatara
This work introduces the M6(GPT)3 composer system, capable of generating complete, multi-minute musical compositions with complex structures in any time signature, in the MIDI doma…