  This study aims to develop an auxiliary diagnostic system for classifying
abnormal lung respiratory sounds, enhancing the accuracy of automatic abnormal
breath sound classification through an innovative multi-label learning approach
and multi-head attention mechanism. Addressing the issue of class imbalance and
lack of diversity in existing respiratory sound datasets, our study employs a
lightweight and highly accurate model, using a two-dimensional label set to
represent multiple respiratory sound characteristics. Our method achieved a
59.2% ICBHI score in the four-category task on the ICBHI2017 dataset,
demonstrating its advantages in terms of lightweight and high accuracy. This
study not only improves the accuracy of automatic diagnosis of lung respiratory
sound abnormalities but also opens new possibilities for clinical applications.
