Stefan Lattner
@stefanlattner.bsky.social
Research Leader @ Sony CSL Paris
🎉 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
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
August 20, 2025 at 2:01 PM
🎉 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
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
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
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
January 24, 2025 at 1:42 PM
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
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/
🎬🎙️ Recording:
echo360.org.uk/media/f037dc...
🎶 More Info:
www.qmul.ac.uk/dmrn/dmrn19/
January 22, 2025 at 1:15 PM
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/
🎬🎙️ 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. 🧵👇
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. 🧵👇
January 21, 2025 at 3:20 PM
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. 🧵👇
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. 🧵👇