31 citations · 89 across the 10 of their papers we have counts for
20 papers · 1 filter
Few-Shot Musical Source Separation
Yu Wang, Daniel Stoller, Rachel M. Bittner +1
Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen inst…
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…
Who calls the shots? Rethinking Few-Shot Learning for Audio
Yu Wang, Nicholas J. Bryan, Justin Salamon +2
Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advance…
Soundata: A Python library for reproducible use of audio datasets
Magdalena Fuentes, Justin Salamon, Pablo Zinemanas +6
Soundata is a Python library for loading and working with audio datasets in a standardized way, removing the need for writing custom loaders in every project, and improving reprodu…
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…