Audio Classification On Icbhi Respiratory

Métriques

ICBHI Score

Sensitivity

Specificity

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	ICBHI Score	Sensitivity	Specificity	Paper Title	Repository
DAT (AST)	59.81	42.50	77.11	Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification	-
AST (Patch-Mix CL)	62.37	43.07	81.66	Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification	-
SG-SCL (AST)	61.71	43.55	79.87	Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification	-
CNN6 (+metadata)	58.04	-	-	Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning	-
AST (fine-tuning)	-	41.97	77.14	Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification	-
ResNeSt	55.30	40.20	70.40	A DOMAIN TRANSFER BASED DATA AUGMENTATION METHOD FOR AUTOMATED RESPIRATORY CLASSIFICATION	-
Audio-CLAP	62.56	44.67	80.85	BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification	-
bi-ResNet (scratch)	50.16	31.10	69.20	LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm
M2D/0.7 (e=0.3)	62.73	-	-	Masked Modeling Duo: Towards a Universal Audio Pre-training Framework	-
CNN6 (scratch)	54.74	33.84	75.35	Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning	-
CycleGuardian	63.26	44.47	82.06	CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning	-
AFT on Mixed-500	61.79	42.86	80.72	Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance	-
ResNet-34	56.20	40.10	72.30	RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting	-
CNN6	57.55	-	75.95	Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning	-
CNN6 (+metadata)	-	39.15	76.93	Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning	-
M2D-X/0.7 (η=0.3)	63.29	-	-	0/1 Deep Neural Networks via Block Coordinate Descent	-
ResNet-50	58.29	37.24	79.34	Lung Sound Classification Using Co-tuning and Stochastic Normalization	-
AST (fine-tuning)	59.55	-	-	Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification	-
BTS	63.54	45.67	81.4	BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification	-
bi-ResNet-Att	56.76	46.38	67.13	ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention	-

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