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.

翻译：心肺音对医疗健康监测至关重要。近年来听诊器技术的进步使得以更高精度采集患者声音成为可能。本数据集采用数字听诊器采集了包括独立与混合录音在内的心音与肺音。据我们所知，这是首个同时提供独立与混合心肺音的数据集。录音采集自临床人体模型——一种能模拟人体生理状态的患者模拟器，可在不同身体部位生成清晰的心肺音。本数据集包含正常音与多种异常音（如心脏杂音、心房颤动、心动过速、房室传导阻滞、第三及第四心音、哮鸣音、湿啰音、干啰音、胸膜摩擦音及咕噜音）。数据集收录了由专科护士确定的不同解剖位置胸部检查的音频记录，每条录音均通过频率滤波器增强以突出特定声音类型。该数据集适用于人工智能领域的多种应用，包括自动心肺疾病检测、声音分类、无监督分离技术以及与音频信号处理相关的深度学习算法。