  Heart sound diagnosis and classification play an essential role in detecting
cardiovascular disorders, especially when the remote diagnosis becomes standard
clinical practice. Most of the current work is designed for single category
based heard sound classification tasks. To further extend the landscape of the
automatic heart sound diagnosis landscape, this work proposes a deep multilabel
learning model that can automatically annotate heart sound recordings with
labels from different label groups, including murmur's timing, pitch, grading,
quality, and shape. Our experiment results show that the proposed method has
achieved outstanding performance on the holdout data for the multi-labelling
task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level,
and an overall accuracy=0.969 at the patient's recording level.
