2.2k citations · 4k across the 30 of their papers we have counts for
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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…
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…
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
Yusong Wu, Josh Gardner, Ethan Manilow +3
Data is the lifeblood of modern machine learning systems, including for those in Music Information Retrieval (MIR). However, MIR has long been mired by small datasets and unreliabl…
Multi-instrument Music Synthesis with Spectrogram Diffusion
Curtis Hawthorne, Ian Simon, Adam Roberts +4
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural…
Improving Source Separation by Explicitly Modeling Dependencies Between Sources
Ethan Manilow, Curtis Hawthorne, Cheng-Zhi Anna Huang +2
We propose a new method for training a supervised source separation system that aims to learn the interdependent relationships between all combinations of sources in a mixture. Rat…