1.4k citations · 1.7k across the 26 of their papers we have counts for
6 papers · 1 filter
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…
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…
Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes
Aurora Cramer, Mark Cartwright, Fatemeh Pishdadian +1
While the estimation of what sound sources are, when they occur, and from where they originate has been well-studied, the estimation of how loud these sound sources are has been of…
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…