stefanlattner.bsky.social
Research Leader @ Sony CSL Paris
13 posts
55 followers
41 following
Getting Started
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We also show that our IC estimates can help predict EEG measurements. 💆♀️
Surprisal can be used for segment boundary detection and to simulate the information processing of a listener. 🎶 🧠
📜 Link to the paper: arxiv.org/pdf/2501.07474
Model weights are soon to come! 🏋️
💫✨ #SonyCSLMusic 💫✨
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3/ Results show:
- Higher fidelity (FAD ↓ by 20%)
- Better adherence to text & audio prompts (APA ↑)
- Faster generation with 5-step inference!
AI-assisted music production. 🎼💡 Let us know your thoughts!
Congrats to the authors Javier Nistal and Marco Pasini!
#AI #MusicGeneration #Transformers
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2/ 🎤 What’s new?
- Stereo output with superior fidelity
- Bridging the gap in Text-to-audio CLAP embeddings 📝🎵
- Faster inference using a consistency framework ⚡
Audio examples: sonycslparis.github.io/improved_dar/ 🎶👂
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1/ Building on Diff-A-Riff, we’ve upgraded to a stereo-capable autoencoder & replaced the U-Net with a Diffusion Transformer (DiT) to improve quality, diversity, and control. 🎧📈 Plus, our model generates high-quality audio with fewer denoising steps. 🚀
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Hybrid Losses for Hierarchical Embedding Learning
H. Tian, S. Lattner, B. McFee, C. Saitis
Congrats to the authors!
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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