3 papers
eess.AS2025
Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification
Francesca Ronchini, Ho-Hsiang Wu, Wei-Cheng Lin +1
This paper investigates the design of effective prompt strategies for generating realistic datasets using Text-To-Audio (TTA) models. We also analyze different techniques for effic…
cs.MM2024
Learning Audio Concepts from Counterfactual Natural Language
Ali Vosoughi, Luca Bondi, Ho-Hsiang Wu +1
Conventional audio classification relied on predefined classes, lacking the ability to learn from free-form text. Recent methods unlock learning joint audio-text embeddings from ra…
cs.SD2023
Audio-Text Models Do Not Yet Leverage Natural Language
Ho-Hsiang Wu, Oriol Nieto, Juan Pablo Bello +1
Multi-modal contrastive learning techniques in the audio-text domain have quickly become a highly active area of research. Most works are evaluated with standard audio retrieval an…