189 citations · 207 across the 12 of their papers we have counts for
12 papers
Live Music Models
Lyria Team, Antoine Caillon, Brian McWilliams +33
We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release M…
Diversity-Rewarded CFG Distillation
Geoffrey Cideron, Andrea Agostinelli, Johan Ferret +5
Generative models are transforming creative domains such as music generation, with inference-time strategies like Classifier-Free Guidance (CFG) playing a crucial role. However, CF…
MAD Speech: Measures of Acoustic Diversity of Speech
Matthieu Futeral, Andrea Agostinelli, Marco Tagliasacchi +2
Generative spoken language models produce speech in a wide range of voices, prosody, and recording conditions, seemingly approaching the diversity of natural speech. However, the e…
MusicRL: Aligning Music Generation to Human Preferences
Geoffrey Cideron, Sertan Girgin, Mauro Verzetti +11
We propose MusicRL, the first music generation system finetuned from human feedback. Appreciation of text-to-music models is particularly subjective since the concept of musicality…
Brain2Music: Reconstructing Music from Human Brain Activity
Timo I. Denk, Yu Takagi, Takuya Matsuyama +4
The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a metho…
SingSong: Generating musical accompaniments from singing
Chris Donahue, Antoine Caillon, Adam Roberts +8
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create mus…
MusicLM: Generating Music From Text
Andrea Agostinelli, Timo I. Denk, Zalán Borsos +10
We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process o…
How stable are Transferability Metrics evaluations?
Andrea Agostinelli, Michal Pándy, Jasper Uijlings +2
Transferability metrics is a maturing field with increasing interest, which aims at providing heuristics for selecting the most suitable source models to transfer to a given target…
Transferability Metrics for Selecting Source Model Ensembles
Andrea Agostinelli, Jasper Uijlings, Thomas Mensink +1
We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the targe…
Transferability Estimation using Bhattacharyya Class Separability
Michal Pándy, Andrea Agostinelli, Jasper Uijlings +2
Transfer learning has become a popular method for leveraging pre-trained models in computer vision. However, without performing computationally expensive fine-tuning, it is difficu…
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli +2
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An altern…
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico +2
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-par…