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Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders in auscultation. Crackles are very common adventitious transient sounds. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases may be assessed. In this study, a method is proposed for crackle detection. In this method, various feature sets are extracted using time–frequency and time–scale analysis from pulmonary signals. In order to understand the effect of using different window and wavelet types in time–frequency and time–scale analysis in detecting crackles, different windows and wavelets are tested such as Gaussian, Blackman, Hanning, Hamming, Bartlett, Triangular and Rectangular windows for time–frequency analysis and Morlet, Mexican Hat and Paul wavelets for time–scale analysis. The extracted feature sets, both individually and as …