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Models/CNN6 (scratch)

CNN6 (scratch)

Reported on 6 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Audio3 results

Audio ClassificationonICBHI Respiratory Sound Database
ICBHI Score· 2022-10-27
54.74
best: 65.53 (ADD)
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning arXiv:2210.16192
Audio ClassificationonICBHI Respiratory Sound Database
Sensitivity· 2022-10-27
33.84
best: 48.21 (BEATs (CE))
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning arXiv:2210.16192
Audio ClassificationonICBHI Respiratory Sound Database
Specificity· 2022-10-27
75.35
best: 85.13 (ADD)
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning arXiv:2210.16192

Methodology3 results

ClassificationonICBHI Respiratory Sound Database
ICBHI Score· 2022-10-27
54.74
best: 65.53 (ADD)
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning arXiv:2210.16192
ClassificationonICBHI Respiratory Sound Database
Sensitivity· 2022-10-27
33.84
best: 48.21 (BEATs (CE))
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning arXiv:2210.16192
ClassificationonICBHI Respiratory Sound Database
Specificity· 2022-10-27
75.35
best: 85.13 (ADD)
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning arXiv:2210.16192