Code repository of our research paper on AI-generated music detection "AI-Generated Music Detection and its Challenges" - D. Afchar, G. Meseguer Brocal, R. Hennequin (accepted for IEEE ICASSP 2025).
We create an AI-music detector by detecting the use of an artificial decoder (e.g., a neural decoder). For that, we auto-encode a dataset of music with several such auto-encoders to train on. This setting enables us to avoid detecting confounding artefacts. For instance, if a dataset of artificial music only contains pop music, you don't want to inadvertently train a pop music detector. Here, the task is to distinguish real music from its reconstructed counterpart. With the same musical content and compression setting, only the autoencoder artefacts remain. We also verify that merely training on autoencoder allows the model to detect music fully-generated from prompts (i.e., not auto-encoded).
Examples of audio reconstructions may be found in the audio_examples folder or on the demo page: research.deezer.com/deepfake-detector/.
The FMA dataset is available at github.com/mdeff/fma.
More than a detector, we ponder the larger consequences of deploying a detector: robustness to manipulation, generalisation to different models, interpretability, ...
Most of our experiment code is available for the review. We will make the trained weights open source for the publication.