  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.
