31 citations · 39 across the 6 of their papers we have counts for
7 papers
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…
How to Listen? Rethinking Visual Sound Localization
Ho-Hsiang Wu, Magdalena Fuentes, Prem Seetharaman +1
Localizing visual sounds consists on locating the position of objects that emit sound within an image. It is a growing research area with potential applications in monitoring natur…
A Study on Robustness to Perturbations for Representations of Environmental Sound
Sangeeta Srivastava, Ho-Hsiang Wu, Joao Rulff +5
Audio applications involving environmental sound analysis increasingly use general-purpose audio representations, also known as embeddings, for transfer learning. Recently, Holisti…
Wav2CLIP: Learning Robust Audio Representations From CLIP
Ho-Hsiang Wu, Prem Seetharaman, Kundan Kumar +1
We propose Wav2CLIP, a robust audio representation learning method by distilling from Contrastive Language-Image Pre-training (CLIP). We systematically evaluate Wav2CLIP on a varie…
Exploring modality-agnostic representations for music classification
Ho-Hsiang Wu, Magdalena Fuentes, Juan P. Bello
Music information is often conveyed or recorded across multiple data modalities including but not limited to audio, images, text and scores. However, music information retrieval re…
Multi-Task Self-Supervised Pre-Training for Music Classification
Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang +4
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data…
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez +9
We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems fo…