ICBHI
PulseAugur coverage of ICBHI — every cluster mentioning ICBHI across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
New APRIL framework enhances respiratory sound classification with domain-incremental learning
Researchers have developed APRIL, a novel framework for domain-incremental learning in respiratory sound classification. This exemplar-free approach utilizes a shared BEATs backbone and low-rank experts to adapt to new …
-
AI models advance respiratory sound classification with new techniques
Two new research papers propose advanced AI techniques for classifying respiratory sounds. One paper introduces QLung, a quality-adaptive framework that adjusts learning margins based on audio recording quality, improvi…
-
AI model tackles stethoscope variations in respiratory sound classification
Researchers have developed a new federated domain generalization framework to improve the accuracy of AI models classifying respiratory sounds, particularly when dealing with variations caused by different stethoscopes.…
-
Meta-ensemble learning boosts respiratory sound classification to new state-of-the-art
Researchers have developed a meta-ensemble learning approach to improve the accuracy of respiratory sound classification models. This method trains base models on varied data splits to enhance prediction diversity, whic…