12 papers
Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew
Joan SerrÃ, Dipam Goswami, Fabio Morreale +2
Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks. Nonetheless, current TDA approaches lack reliabili…
Woosh: A Sound Effects Foundation Model
Gaëtan Hadjeres, Marc Ferras, Khaled Koutini +7
The audio research community depends on open generative models as foundational tools for building novel approaches and establishing baselines. In this report, we present Woosh, Son…
Leveraging Whisper Embeddings for Audio-based Lyrics Matching
Eleonora Mancini, Joan SerrÃ, Paolo Torroni +1
Audio-based lyrics matching can be an appealing alternative to other content-based retrieval approaches, but existing methods often suffer from limited reproducibility and inconsis…
Emergent, not Immanent: A Baradian Reading of Explainable AI
Fabio Morreale, Joan SerrÃ, Yuki Mitsufuji
Explainable AI (XAI) is frequently positioned as a technical problem of revealing the inner workings of an AI model. This position is affected by unexamined onto-epistemological as…
Automatic Music Mixing using a Generative Model of Effect Embeddings
Eloi Moliner, Marco A. MartÃnez-RamÃrez, Junghyun Koo +5
Music mixing involves combining individual tracks into a cohesive mixture, a task characterized by subjectivity where multiple valid solutions exist for the same input. Existing au…
Automatic Music Sample Identification with Multi-Track Contrastive Learning
Alain Riou, Joan SerrÃ, Yuki Mitsufuji
Sampling, the technique of reusing pieces of existing audio tracks to create new music content, is a very common practice in modern music production. In this paper, we tackle the c…