Environment Sound Classification
1 papers with code • 1 benchmarks • 1 datasets
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Benchmarks
These leaderboards are used to track progress in Environment Sound Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ESC-50
Most implemented papers
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99. 75\%, 93. 4\%, and 86. 49\% respectively.
