activity
20192023
most citedMusicLM: Generating Music From Text

189 citations · 278 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SD2023★ 2 cited

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…

cs.SD2023★ 50 cited

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…

cs.SD2023★ 189 cited

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…

eess.AS2022★ 31 cited

MuLan: A Joint Embedding of Music Audio and Natural Language

Qingqing Huang, Aren Jansen, Joonseok Lee +3

Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This pa…

eess.AS2022

Text-Driven Separation of Arbitrary Sounds

Kevin Kilgour, Beat Gfeller, Qingqing Huang +3

We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is…

cs.LG2020★ 6 cited

Superbloom: Bloom filter meets Transformer

John Anderson, Qingqing Huang, Walid Krichene +2

We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash to…

cs.LG2019

Gradient-based Optimization for Bayesian Preference Elicitation

Ivan Vendrov, Tyler Lu, Qingqing Huang +1

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and con…