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Models/CycleGuardian

CycleGuardian

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

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
Specificity· 2025-02-02
82.06
best: 85.13 (ADD)
SOTA
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning arXiv:2502.00734
Audio ClassificationonICBHI Respiratory Sound Database
ICBHI Score· 2025-02-02
63.26
best: 65.53 (ADD)
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning arXiv:2502.00734
Audio ClassificationonICBHI Respiratory Sound Database
Sensitivity· 2025-02-02
44.47
best: 48.21 (BEATs (CE))
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning arXiv:2502.00734

Methodology3 results

ClassificationonICBHI Respiratory Sound Database
Specificity· 2025-02-02
82.06
best: 85.13 (ADD)
SOTA
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning arXiv:2502.00734
ClassificationonICBHI Respiratory Sound Database
ICBHI Score· 2025-02-02
63.26
best: 65.53 (ADD)
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning arXiv:2502.00734
ClassificationonICBHI Respiratory Sound Database
Sensitivity· 2025-02-02
44.47
best: 48.21 (BEATs (CE))
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning arXiv:2502.00734