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Found 12 papers, 2 papers with code
V2Meow: Meowing to the Visual Beat via Video-to-Music Generation
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures.
Noise2Music: Text-conditioned Music Generation with Diffusion Models
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts.
Ranked #2 on
Text-to-Music Generation
on MusicCaps
MusicLM: Generating Music From Text
We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff".
Ranked #8 on
Text-to-Music Generation
on MusicCaps
MAQA: A Multimodal QA Benchmark for Negation
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs).
MuLan: A Joint Embedding of Music Audio and Natural Language
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.
Text-Driven Separation of Arbitrary Sounds
The second model, SoundFilter, takes a mixed source audio clip as an input and separates it based on a conditioning vector from the shared text-audio representation defined by SoundWords, making the model agnostic to the conditioning modality.
Superbloom: Bloom filter meets Transformer
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids.
Gradient-based Optimization for Bayesian Preference Elicitation
Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational.
Recovering Structured Probability Matrices
When can accurate reconstruction be accomplished in the sparse data regime?
Super-Resolution Off the Grid
- The number of measurements taken by and the computational complexity of our algorithm are bounded by a polynomial in both the number of points k and the dimension d, with no dependence on the separation \Delta.
Learning Mixtures of Gaussians in High Dimensions
Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case.
Minimal Realization Problems for Hidden Markov Models
Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM).
