🚨Excited to share LiveBand 🎶: real-time interactive, live music accompaniment generation in the audio domain running on consumer hardware! Big up to @MarcoPasini and the rest of the team! info👇

Excited to share LiveBand, the first tool that enables robust, real-time jamming with an AI audio generator! 💫 🎸 LiveBand runs locally on a Mac, responds instantly to a musician’s input, and can be guided using either audio references or text prompts. The result is a highly versatile musical partner that you can actually jam with. 🚀 It opens up new possibilities for live performance, creative experimentation, and practicing your improvisation skills. We believe that this is a real game changer for human–AI musical interaction! 📜 Paper: arxiv.org/abs/2606.03803v2 🎶 Audio Samples: sonycslparis.github.io/liveb… 📽️ Video: 👇 Made possible by the great Sony CSL Music Team: 👉 @marco_ppasini 👈 (!), @latentspaces , Ben Hayes, @cyranaouameur & @BjareMathias

Final push! We need a few more participants for a 15-minute listening test: golisten.ucd.ie/task/mushra-… Already helped and coming to #ISMIR? Beer on me :)

Listening test alert 🚨 we need you! 😊 Super simple music denoising test: listen to a few piano clips and rate their closeness to a reference. 15 minutes for you, tons of help for us :) Headphones recommended. Computer needed. lnkd.in/d6t8UzaC Thanks 🫶

🎶 New ISMIR 2025 paper! "Estimating Musical Surprisal from Audio in Autoregressive Diffusion Model Noise Spaces" by Mathias Rose Bjare (JKU), Stefan Lattner (Sony CSL), and Gerhard Widmer (JKU/LIT AI Lab). We explore how surprisal — the unexpectedness of musical events — can be modeled directly from audio using autoregressive diffusion models (ADMs). 💡 What we did: - Compared surprisal from diffusion models vs. Generative Infinite-Vocabulary Transformers (GIVT). - Evaluated across tasks: monophonic pitch surprisal (expectation) & segment boundary detection (structural surprise). - Tested surprisal at different noise levels in diffusion processes to see how musical features emerge at multiple granularities. 🔥 Key take-aways: - Diffusion surprisal beats GIVT in modeling pitch expectation & boundary detection. - Mid-level noise surprisal captures pitch-level expectations while suppressing timbre-related variation. - Surprisal curves align with human-like musical segmentation, showing potential as proxies for perceptual surprise. Why it matters: Understanding musical surprisal links computational modeling with human perception and cognition — with applications in AI composition, real-time music interaction, and brain-music studies. 📜 Paper: arxiv.org/abs/2508.05306 💻 Code: github.com/SonyCSLParis/audi… #DiffusionModels #Surprisal #MIR @ISMIRConf @SonyCSLParis @SonyCSLMusic

❌ We don’t need no negative samples ❌ We don’t need no large batches ❌ No modality gap in the classroom Very happy to introduce SLAP, our latest brick in the wall of multimodal SSL 🎶🧠 Joint work with King @Juj_guinot, accepted at #ISMIR2025! 🇰🇷 1/7

Hey! Our paper, 🌸 “LiLAC: A Lightweight Latent ControlNet for Musical Audio Generation” got accepted as ISMIR 2025! It presents a novel control paradigm for audio diffusion models with greatly reduced parameters, encouraging users to train individual, modular models.

🎭💻 We’re participating in #CultTech Residency — a new program supporting artists and creative teams exploring performance & technology Experience the benefits of working with our AI-powered tool #DiffARiff Feel free to share the news 🔽 Open call: culttech.at/residence-openca…

🌟 New @ieeeICASSP Paper Announcement 🌟 We introduce "Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding", a novel autoencoder achieving higher fidelity at extreme compression rates using consistency models & summary embeddings. 🧵

😃Accepted @ieeeICASSP papers of @SonyCSLMusic: 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 Hybrid Losses for Hierarchical Embedding Learning H. Tian, S. Lattner, B. McFee, C. Saitis Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding M. Pasini, S. Lattner, G. Fazekas Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures A. Riou, S. Lattner, A. Gagneré, G. Hadjeres, S. Lattner, G. Peeters Congrats to the authors! @latentspaces @howariou @GiorgiaCanti @tiianhk @marco_ppasini @GeoffroyPeeters @gaetan_hadjeres @SonyCSLParis

💡If you missed it, we released our new AI-tool, #DrumGAN, which allows you to generate flexible drum sounds with ease Here is a full blogpost for a detailed look at it free download the 1st #AI-drum kits made by #Twenty9 during our collaboration ➡️🎁 tinyurl.com/blpodr

Happy to announce that our legendary DrumGAN, developed 2020 by Javier Nistal @latentspaces is now available as a web application. Free to use and without registration! 🥁 Give it a try at drumgan.csl.sony.fr/ #DrumGAN @SonyCSLMusic

🥳 New publication announcement! Marco Pasini solved the problem of error accumulation in continuous autoregressive models (CAMs), making it possible to generate sequences without the need for prior tokenization. Say goodbye to RVQ codecs (use music2latent 😉). @SonyCSLMusic

🌟 Exciting news! The YouTube channel @aisearchio just released a review of #DiffARiff, and it's already making waves—10k views in less than 12 hours and more than 100 comments with fantastic feedback! 🎉 Thanks so much for the incredible exposure!

🤩 It's exciting to see how Diff-A-Riff makes it easy to build up a song based on some input audio! 👇

🚨 New Video Drop! 🚨 My brother, Carli Nistal, used Diff-A-Riff to create this banger! 🎶🤖 Watch it in action!

🎉New #DiffARiff audio demo 🎚️We continue to introduce you to Diff-A-Riff's capabilities, our accompaniment co-creation tool, with this new #Disco Demo produced by @CarliNistal 👏 Find the HQ video & audio on our YouTube channel #aimusic #research #innovation #sonycslparis