arXiv AI
APRIL: Ambiguity-Aware Expert Routing for Respiratory Sound Domain-Incremental Learning
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Parameter isolation with domain-specific experts for incremental audio classification
The paper introduces a domain‑specific parameter‑isolation architecture for domain‑incremental learning (DIL) in audio classification, aiming to preserve knowledge from earlier domains without accessing their data. By employing data‑free generative replay and cross‑domain feature generation, the method constructs new experts conditioned on all previously frozen models, thereby mitigating catastrophic forgetting. Applied to the DCASE 2026 Challenge Task 7, the approach achieves micro and macro accuracies of 78.4 % and 78.9 %, outperforming the baseline by 33 and 25 percentage points, respectively, with ablation studies confirming the contribution of each component.
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arXiv AI
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RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification
arXiv:2610.15702v1 Announce Type: cross Abstract: Respiratory auscultation provides a low-cost means of assessing pulmonary function, yet transient crackles and sustained wheezes can be difficult to...
arXiv AI
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Do SpeechLMs Hear Their Own Opinions? Diagnosing and Mitigating Previous-Belief Contamination in Streaming Emotion Understanding
The paper investigates how streaming emotion recognition models can be misled by their own prior predictions, a problem termed previous-belief contamination (PBC). Using a counterfactual diagnostic on CREMA-D-Stream, the authors show that feeding a model’s previous emotion label into its current prediction can drastically lower accuracy and flip many predictions, with the effect varying by label. To mitigate PBC, they propose EmoUpdate, a training‑free framework that isolates current audio perception from historical context through a prior‑blind firewall, a causal belief filter, and a decontamination operator, achieving significant gains across multiple SpeechLMs and benchmarks.
arXiv AI
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Feature Scaling Beats Architecture Choice in Offline Respiratory Screening for Low-Resource Clinics
The study compares classical and deep learning classifiers for a five‑class respiratory‑sound dataset of 1,211 clips. An SVM using 30 summary features (MFCC, ZCR, RMS) achieves macro recall of 0.78, while a 1.13M‑parameter convolutional‑recurrent network reaches 0.75–0.77; no clear advantage is found between model families. The largest performance gain comes from standardizing the SVM’s features, which boosts macro recall by 27 points, far exceeding any architectural difference.
arXiv AI
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Investigating catastrophic forgetting in sound event classification
The paper explores methods to mitigate catastrophic forgetting in incremental learning for sound event classification. It evaluates architectural and regularization strategies on FSD50K and AudioSet, finding that deeper layers, especially the classifier head, are most vulnerable. The most effective approach identified is fully freezing the feature extractor while fine‑tuning a dynamic head, which achieves minimal forgetting, stable training, and a balanced trade‑off between memory stability and learning plasticity.