Clinically Informed Acoustic-Aware Attribute Modeling for Respiratory Sound Classification

Xinhong Li; Li Xiao; Ziyang Chen; Yuhong Yang; Weiping Tu

cs.SDOct 16, 2026

Clinically Informed Acoustic-Aware Attribute Modeling for Respiratory Sound Classification

Authors: Xinhong Li, Li Xiao, Ziyang Chen, Yuhong Yang, Weiping Tu

Organizations: School of Computer Science, Wuhan University, Wuhan, China · Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China

Abstract

Automated respiratory sound classification (RSC) aims to identify normal and adventitious respiratory sounds, such as crackles and wheezes. Most existing methods formulate RSC as a flat four-class classification problem over norma, crackle, wheeze and both. However, unlike conventional classification tasks with semantically independent and mutually exclusive categories, the commonly used four-class RSC labels have a compositional structure and can be represented by two clinically meaningful sound attributes: crackle presence and wheeze presence. Existing flat classifiers do not explicitly exploit this structure, which may increase the learning burden by treating the four labels as independent targets. Meanwhile, crackles and wheezes exhibit distinct temporal characteristics, with sparse transient crackle events and sustained continuous wheeze patterns, making a single temporal aggregation strategy less suitable for modeling different adventitious sound evidence. To address these limitations, we propose an Acoustic-Aware Asymmetric Framework (AAAF) for RSC. AAAF reformulates four-class recognition as two sound-attribute detection tasks and recovers the final category through logical composition during inference. Built upon a pretrained BEATs encoder, AAAF employs branch-specific multi-layer fusion and asymmetric temporal aggregation to capture localized crackle evidence and sustained wheeze patterns. Auxiliary normal--abnormal supervision and a batch-adaptive dynamic loss are further introduced to improve global discrimination and robustness under imbalanced labels. Experiments on ICBHI and SPRSound demonstrate that AAAF improves overall RSC performance and generalizes across datasets, achieving a state-of-the-art four-class Score of 65.30% on ICBHI.

Figures & tables

Fig. 1: Motivation of AAAF: flat four-class RSC versus crackle–wheeze attribute modeling with acoustic-aware temporal aggregation.

Fig. 2: Overview of the proposed AAAF. Hierarchical BEATs representations are fused and fed into two attribute-specific branches for crackle and wheeze presence detection. Temporal Top- $K$ pooling emphasizes localized crackle evidence, while average pooling with channel attention captures sustained wheeze patterns. The final four-class prediction is obtained by logical composition, with auxiliary normal–abnormal supervision and batch-adaptive dynamic loss for joint optimization.

	Method	Architecture	Pretrain	Venue	$S_{p}$ (%)	$S_{e}$ (%)	Score (%)
4-class eval.	Patch-Mix [ 15 ]	AST	IN + AS	INTERSPEECH’23	81.66	43.07	62.37
	SG-SCL [ 6 ]	AST	IN + AS	ICASSP’24	79.87	43.55	61.71
	RepAugment [ 14 ]	AST	IN + AS	EMBC’24	82.47	40.55	61.51
	LungAdapter [ 5 ]	AST	IN + AS	INTERSPEECH’24	80.43	44.37	62.40
	BTS [ 21 ]	CLAP	LA	INTERSPEECH’24	81.40	45.67	63.54
	AMG-SCL [ 22 ]	AST	IN + AS	JBHI’25	76.18	47.91	62.04

TABLE I: Performance comparison on the ICBHI 2017 benchmark. Bold and underline indicate the best and second-best results, respectively. IN, AS, and LA denote ImageNet [ 18 ] , AudioSet [ 19 ] , and LAION-Audio-630K [ 20 ] pretraining.

Method	Architecture	Pretrain	4-class eval.			2-class eval.
Method	Architecture	Pretrain	$S_{p}$ (%)	$S_{e}$ (%)	Score (%)	$S_{p}$ (%)	$S_{e}$ (%)	Score (%)
ResNet-50 †	CNN	IN	91.33	82.81	87.07	89.72	90.07	89.90
EfficientNet-B0 †	CNN	IN	91.68	83.57	87.63	90.30	91.15	90.72
AST †	AST	IN + AS	90.85	84.65	87.75	89.80	92.57	91.19
BEATs †	BEATs	AS	91.39	85.36	88.38	90.06	93.99	92.02
SCL [ 4 ]	CNN	AS	90.49	80.69	85.59	–	–	–

TABLE II: External validation results on the SPRSound dataset.

\dagger

denotes results reproduced by us under the same evaluation protocol. Pretraining abbreviations follow Table I .

Modules				Performance (%)
Multi	AATA	Aux	DyLoss	$S_{p}$	$S_{e}$	Score
				78.77 $\pm$ 3.07	48.21 $\pm$ 2.32	63.49 $\pm$ 1.08
✓				76.35 $\pm$ 2.60	50.55 $\pm$ 2.24	63.45 $\pm$ 0.77
✓	✓			82.84 $\pm$ 3.03	45.74 $\pm$ 3.30	64.29 $\pm$ 0.58
✓	✓	✓		80.59 $\pm$ 3.60	48.39 $\pm$ 3.47	64.49 $\pm$ 0.08
✓	✓		✓	77.48 $\pm$ 0.85	51.78 $\pm$ 1.95	64.63 $\pm$ 0.83

TABLE III: Ablation study of AAAF components. Multi: multi-layer fusion; AATA: acoustic-aware asymmetric temporal aggregation; Aux: auxiliary task; DyLoss: dynamic batch-adaptive loss.

Layer Selection	$S_{p}$ (%)	$S_{e}$ (%)	Score (%)
Last Layer	76.81 $\pm$ 2.18	51.35 $\pm$ 3.20	64.08 $\pm$ 0.81
Interval Layers	79.59 $\pm$ 3.13	49.21 $\pm$ 3.67	64.40 $\pm$ 0.67
All Layers	77.39 $\pm$ 2.23	49.21 $\pm$ 2.65	63.30 $\pm$ 0.62
Ours	80.25 $\pm$ 3.57	50.35 $\pm$ 3.15	65.30 $\pm$ 0.50

TABLE IV: Comparison of BEATs layer selection strategies for hierarchical feature extraction.

$K$	$S_{p}$ (%)	$S_{e}$ (%)	Score (%)
1	82.13 $\pm$ 4.03	46.29 $\pm$ 3.77	64.21 $\pm$ 0.84
3	79.38 $\pm$ 2.68	50.09 $\pm$ 1.74	64.74 $\pm$ 0.64
5	80.25 $\pm$ 3.57	50.35 $\pm$ 3.15	65.30 $\pm$ 0.50
7	79.54 $\pm$ 7.57	48.84 $\pm$ 6.35	64.19 $\pm$ 1.00
9	84.23 $\pm$ 6.32	45.18 $\pm$ 7.19	64.70 $\pm$ 0.94
11	80.11 $\pm$ 5.15	49.02 $\pm$ 4.65	64.57 $\pm$ 0.50

TABLE V: Impact of Top-

K

pooling in the crackle branch.

Fig. 3: Frame-wise branch responses on a Both sample before temporal pooling. The crackle branch produces sparse peak responses around localized transient regions, with selected Top- $K$ frames marked by dots, while the wheeze branch shows broader sustained activations over longer intervals.

Backbone	Method	$S_{p}$ (%)	$S_{e}$ (%)	Score(%)
ResNet-50	CE	76.53 $\pm$ 4.56	32.01 $\pm$ 2.95	54.24 $\pm$ 1.00
ResNet-50	AAAF	79.11 $\pm$ 2.02	31.47 $\pm$ 2.71	55.29 $\pm$ 0.71
Eff.-B0	CE	77.63 $\pm$ 3.94	37.72 $\pm$ 3.19	57.68 $\pm$ 0.79
Eff.-B0	AAAF	86.92 $\pm$ 0.87	31.05 $\pm$ 1.46	58.98 $\pm$ 0.66
AST	CE	73.12 $\pm$ 3.38	45.91 $\pm$ 2.19	59.52 $\pm$ 0.84
AST	AAAF	75.27 $\pm$ 4.46	47.03 $\pm$ 5.19	61.15 $\pm$ 0.51

TABLE VI: Backbone generalization on ICBHI four-class evaluation.

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PAC-KD: Pathology-Aware Compositional Knowledge Distillation for Respiratory Sound Classification

Li Xiao, Weiping Tu, Youqiang Zheng, Dan Chen

School of Computer Science, Wuhan University, Wuhan 430072, China

Respiratory Sound Classification Knowledge Distillation Multi-Teacher Knowledge Distillation

Open paper AskPDF arXiv

Respiratory sound classification distinguishes four categories: Normal, Crackle, Wheeze, and Both (crackle and wheeze). Existing knowledge distillation transfers four-class predictions without explicitly exploiting shared respiratory events, potentially limiting the transfer of event-specific knowledge across individual abnormal classes and the Both class. We propose PAC-KD, a pathology-aware compositional knowledge distillation framework that learns crackle and wheeze presence through two dedicated specialists. Their complementary knowledge is transferred to a unified four-class student, enabling it to effectively capture pathology-specific attributes while preserving class-level discrimination. Furthermore, stable conflict calibration adaptively regulates specialist guidance according to its consistency with classification supervision, mitigating potential supervision conflicts. Experiments on ICBHI and SPRSound achieve Scores of 65.28% and 89.54%, respectively, outperforming strong knowledge distillation baselines on both datasets.

Abstract

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