Lung Auscultation is a non-invasive process of distinguishing normal respiratory sounds from abnormal ones by analyzing the airflow along the respiratory tract. With developments in Deep Learning and wider access to anonymized data, automatic detection of specific sounds such as crackles and wheezes have been gaining popularity. We propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep-encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model, trained using sounds from daily activities. The first set of biomarkers highlight the time frequency localization characteristics obtained from DWT coefficients; whereas, the second set of deep encoded biomarkers captures a generalized reliable representation, and thus indemnifies the scarcity of training samples and the class imbalance in dataset. Furthermore, the ensemble of DWT based time-frequency localization and generic device agnostic deep embedding resulted into complimenting each other. We evaluated our approach on [International Conference on Biomedical Health Informatics]
ICBHI-2017 Challenge dataset. The results shows that our system clearly outperforms the state-of-the-art with a significant margin.