1 citations · 2 across the 4 of their papers we have counts for
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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…
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…