The recording of respiratory sounds was of significant benefit in the diagnosis of abnormalities in respiratory sounds. The duration of the sounds used in the diagnosis affects the speed of the diagnosis. In this study, the effect of window size on diagnosis of abnormalities in respiratory sounds and the most efficient recording time for diagnosis were analyzed. First, window size was applied to each sound in the data set consisting of normal and abnormal breathing sounds, 0.5 second and from 1 to 20 seconds Increased by 1 second. Then, the data applied to window size was inferred feature extraction with Mel Frequency Cepstral Coefficient (MFCC) and the performance of each window was calculated using the leave one-out classifier and the k-nearest neighbor (KNN) algorithm. As a result, it was determined that the data was significant with an average performance of 92.06% in the records between 2 and 10 seconds.

翻译：呼吸声录音在诊断呼吸声异常方面有重大益处。诊断中所用声音的持续时间影响诊断速度。在这项研究中,分析了窗口大小对呼吸声异常诊断的影响和最有效的诊断记录时间。首先,对数据集中由正常和异常呼吸声音组成的每声音应用窗口大小,0.5秒和1至20秒增加1秒。然后,对窗口大小应用的数据被推断为与Mel Riot Cepstraal Covali(MFCC)的特征提取,而每个窗口的性能是使用请假一次性分类器和Knearest邻居(KNN)算法计算出来的。结果确定,在2至10秒的记录中,平均性能为92.06%。