When training a model for anomalous sound detection, one usually needs to estimate the
underlying distribution of the normal data. By doing so, anomalous data has a lower …

This paper proposes an approach for anomalous sound detection that incorporates outlier
exposure and inlier modeling within a unified framework by multitask learning. While outlier …

Auscultation with a stethoscope is the most conventional means of examining and obtaining
first insights into cardiovascular and pulmonary disorders. Although being noninvasive and …

The principal method for screening and diagnosing lung disorders is auscultation of
respiratory sounds. In the medical field, automated auscultation is a new research field that …

Automated respiratory sound classification aims to provide a rapid and reliable diagnosis of
respiratory disease. However, the database used to develop a lung sounds classification …

The interpretation of lung auscultation is highly subjective and relies on non-specific
nomenclature. Computer-aided analysis has the potential to better standardize and …

Large annotated lung sound databases are publicly available and might be used to train
algorithms for diagnosis systems. However, it might be a challenge to develop a well …

Anomalous audio in speech recordings is often caused by speaker voice distortion, external
noise, or even electric interferences. These obstacles have become a serious problem in …

Respiratory diseases are the world's third leading cause of mortality. Early detection is
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …

Pattern recognition from audio signals is an active research topic encompassing audio
tagging, acoustic scene classification, music classification, and other areas. Spectrogram …