Cardiac auscultation is an effective method to screen hemodynamic abnormalities. This paper aims to propose an automated algorithm to identify the presence of murmurs in heart sounds from multiple auscultation locations and to determine whether the heart sounds signal is normal. Two methods are explored. In method one, we perform a series of pre-processing such as denoising and segmentation of the heart sounds signal, extract Log Mel-Spectrogram as features, and use fastai's built-in xResNet 18 pre-trained model for classification. In method two, we extract Mel-frequency cepstral coefficients (MFCCs) as features without any pre-processing and build a customized model based on deep residual networks using one-dimensional convolutional neural layers. Our team, USST_Med, received a challenging score of weighted accuracy of 0.56 and cost of 11114.003 on the validation dataset. The proposed method has potential for clinical application.