Respiratory sounds are best heard with stethoscope. This is what is called lung auscultation, A technique used by physicians to detect the abnormal respiratory sounds and thus diagnose the underlying respiratory ailments. Now, these abnormal respiratory sounds are the early signs of not only the presence, but the progression of underlying respiratory ailments like asthma, chronic bronchitis and many more. In this paper, we explore a different set of acoustic biomarkers that can automatically detect these abnormal respiratory sounds. So the proposed system combines 2 approaches to perform the automatic respiratory sound. Event detection. In first approach we use the white set of discrete wavelet transformation that is DWT based descriptor to explore the richness of handcrafted tempo spectral representation for respiratory sounds. In second approach a transfer learning using pre trained audio event detection AED model is used to dump this stream the respiratory sound event detection task now unlike the previous deep learning based approaches. We here use a simple DNN model to train the DWT coefficients without any data augmentation. Also are proposed Deep encoded features with computationally simple architecture are able to compensate the lack of training samples as well as the imbalance between healthy and unhealthy class, which is one of the major challenges. While we deal with the medical data, both of our proposed approaches are shown to be complementary. In detecting the abnormal respiratory sounds from bridge cycles. We believe this is a promising step towards a non invasive real time diagnosis of pulmonary disease using lung auscultation along with automation. Our proposed system is inexpensive yet reliable, thus can impact the lives of many people and reduce clinical intervention.