🎶 New paper out! Diffusion Timbre Transfer via Mutual Information Guided Inpainting Training-free timbre transfer with diffusion models: preserve melody & rhythm, edit timbre at inference time using MI-guided noise and clamping. 📄 arxiv.org/abs/2601.01294 #DiffusionModels #AudioML #GenAI #MIR

🎉 New ISMIR 2025 paper! Autoregressive Diffusion Models estimate musical surprisal more effectively than GIVT — capturing pitch expectations & segment boundaries 🎶 📜 arxiv.org/abs/2508.05306 #ListenerModels #Diffusion #ISMIR2025 @sonycsl-paris.bsky.social

🎶 New paper alert! Do AI audio embeddings *hear* timbre like we do? ➡️ Benchmarked 18 reps vs 2.6 K human ratings (21 datasets) 🏅 Style embeddings from CLAP & our sound-matching model are best aligned! Paper: arxiv.org/abs/2507.07764 #ISMIR2025 #MIR #AudioAI #SonyCSLMusic

As Sony Techhub went offline, here is the direct link to DrumGAN: drumgan.csl.sony.fr

🔥Visit our talks and posters at #ICASSP2025! 👀 Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding M. Pasini, S. Lattner, G. Fazekas Wednesday, April 9 ( pm): Deep generative models I

🤩 From our series "@ieeeICASSP paper released", we announce that "Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures" is online! 📜 Paper: arxiv.org/pdf/2411.19806 Thx to my colleagues Alain Riou, Geoffroy Peeters, Gaetan Hadjeres and Antonin Gagneré! 🎶 SonyCSLMusic 🎶

Our #ICASSP paper "Hybrid Losses for Hierarchical Embedding Learning" by Haokun Tian et al. is now online! 💫 We assess the organization of a hierarchical embedding space using different (combinations of) losses and improve on the SOTA. 📜 Paper: arxiv.org/pdf/2501.12796 #SonyCSLParis

Recently, I had the honour of giving a keynote speech on Audio Representation Learning and Generation at the DMRN+ workshop at @c4dm at Queen Mary University. 💫 🎬🎙️ Recording: echo360.org.uk/media/f037dc... 🎶 More Info: www.qmul.ac.uk/dmrn/dmrn19/

Our #ICASSP paper "Estimating Musical Surprisal in Audio" is now online. 😯 <- surprised 😁 Great work by Mathias Bjare and Giorgia Cantisani! 👏 We use an autoregressive transformer and Gaussian mixture models to estimate the information content in music2latent representations. 🧵👇

🎶✨ New Paper Announcement! ✨🎶 We present "Improving Musical Accompaniment Co-creation via Diffusion Transformers" 🎹🎸—a study advancing our Diff-A-Riff stem generator through improved quality, efficiency, and control. 📜Read the full paper here: arxiv.org/pdf/2410.23005 🧵👇

🧑‍🎓 Our #ISMIR Conference Tutorial "Deep Learning 101 for Audio-based MIR" provides a broad introduction to music audio processing, analysis, and generation. 📘 The book and jupyter notebooks: geoffroypeeters.github.io/deeplearning... 🎥 The recording of the tutorial: us02web.zoom.us/rec/share/Qz...

😃 Accepted #ICASSP papers of Sony CSL Music Team: Accompaniment Prompt Adherence: A Measure for Evaluating Music Accompaniment Systems M. Grachten, J. Nistal Estimating Musical Surprisal in Audio M. Bjare, G. Cantisani, S. Lattner and G. Widmer