189 citations · 313 across the 8 of their papers we have counts for
9 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…
Diff4Steer: Steerable Diffusion Prior for Generative Music Retrieval with Semantic Guidance
Xuchan Bao, Judith Yue Li, Zhong Yi Wan +5
Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs. To address t…
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…
V2Meow: Meowing to the Visual Beat via Video-to-Music Generation
Kun Su, Judith Yue Li, Qingqing Huang +8
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures. While recent music generation models…
Noise2Music: Text-conditioned Music Generation with Diffusion Models
Qingqing Huang, Daniel S. Park, Tao Wang +12
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator…
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…
Outfit Generation and Recommendation -- An Experimental Study
Marjan Celikik, Matthias Kirmse, Timo Denk +4
Over the past years, fashion-related challenges have gained a lot of attention in the research community. Outfit generation and recommendation, i.e., the composition of a set of it…
Contextual BERT: Conditioning the Language Model Using a Global State
Timo I. Denk, Ana Peleteiro Ramallo
BERT is a popular language model whose main pre-training task is to fill in the blank, i.e., predicting a word that was masked out of a sentence, based on the remaining words. In s…
BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding
Timo I. Denk, Christian Reisswig
For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a…