Name
Abstract | ||
|---|---|---|
Few-shot Acoustic Event Classification (AEC) aims to learn a model to recognize novel acoustic events using very limited labeled data. Previous works utilize supervised pre-training as well as meta-learning approaches, which heavily rely on labeled data. Here, we study unsupervised and semi-supervised learning approaches for few-shot AEC. Our work builds upon recent advances in unsupervised repres... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414546 | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Keywords | DocType | ISBN |
Conferences,Buildings,Speech recognition,Semisupervised learning,Signal processing,Performance gain,Acoustics | Conference | 978-1-7281-7605-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsin-Ping Huang | 1 | 0 | 0.68 |
| Krishna C. Puvvada | 2 | 0 | 0.34 |
| Ming Sun | 3 | 91 | 16.25 |
| Chao Wang | 4 | 895 | 190.04 |