189 citations · 374 across the 5 of their papers we have counts for
5 papers
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…
AudioPaLM: A Large Language Model That Can Speak and Listen
Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen +27
We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and…
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…
SPICE: Self-supervised Pitch Estimation
Beat Gfeller, Christian Frank, Dominik Roblek +3
We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations…