1.8k citations · 1.9k across the 13 of their papers we have counts for
13 papers
Improved Robustness in AI-Generated Music Detection
Emile Dugelay, Thomas Barand, Aurélien Laouar +3
AI music generators leave predictable spectral artifacts determined by their architecture. Existing detectors exploit these artifacts with near-perfect accuracy on raw generated tr…
Finding the noise: Zero-shot AI Music Detection
Darius Afchar, Romain Hennequin
We present a novel method for AI-generated music detection in scenarios where the models that generated the input samples are unknown to the detector (e.g., from a newly released s…
A Fourier Explanation of AI-music Artifacts
Darius Afchar, Gabriel Meseguer-Brocal, Kamil Akesbi +1
The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringe…
Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs
Marta Moscati, Darius Afchar, Markus Schedl +1
Humans have the tendency to discover and explore. This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users.…
AI-Generated Music Detection and its Challenges
Darius Afchar, Gabriel Meseguer-Brocal, Romain Hennequin
In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance. In particular, the ability to create credib…
Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation
Elena V. Epure, Gabriel Meseguer-Brocal, Darius Afchar +1
Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categ…