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Found 5 papers, 1 papers with code
TACO: Training-free Sound Prompted Segmentation via Deep Audio-visual CO-factorization
Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of applications.
An Eye for an Ear: Zero-shot Audio Description Leveraging an Image Captioner using Audiovisual Distribution Alignment
In this work, we introduce a novel methodology for bridging the audiovisual modality gap by matching the distributions of tokens produced by an audio backbone and those of an image captioner.
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
First, we find that the formatting of the prompts significantly affects performance so that simply prompting the models with properly formatted class labels performs competitively with optimized prompt templates and even prompt ensembling.
Ranked #1 on
Zero-shot Audio Classification
on ESC-50
On the choice of the optimal temporal support for audio classification with Pre-trained embeddings
Choosing the best one for a set of tasks is the subject of many recent publications.
Foreground-Background Ambient Sound Scene Separation
Ambient sound scenes typically comprise multiple short events occurring on top of a somewhat stationary background.
