Heart and lung sounds are crucial for healthcare monitoring. Recent
improvements in stethoscope technology have made it possible to capture patient
sounds with enhanced precision. In this dataset, we used a digital stethoscope
to capture both heart and lung sounds, including individual and mixed
recordings. To our knowledge, this is the first dataset to offer both separate
and mixed cardiorespiratory sounds. The recordings were collected from a
clinical manikin, a patient simulator designed to replicate human physiological
conditions, generating clean heart and lung sounds at different body locations.
This dataset includes both normal sounds and various abnormalities (i.e.,
murmur, atrial fibrillation, tachycardia, atrioventricular block, third and
fourth heart sound, wheezing, crackles, rhonchi, pleural rub, and gurgling
sounds). The dataset includes audio recordings of chest examinations performed
at different anatomical locations, as determined by specialist nurses. Each
recording has been enhanced using frequency filters to highlight specific sound
types. This dataset is useful for applications in artificial intelligence, such
as automated cardiopulmonary disease detection, sound classification,
unsupervised separation techniques, and deep learning algorithms related to
audio signal processing.