Composites, in the industry, are substituting metal parts in order to reduce the weight of the structures and due to their properties such as stiffness, strength, and others. Acoustic emission (AE) is a well-known and appropriate method for monitoring structures for defects, and it can be successfully applied to composite parts of a structure. Machine learning can be employed in order to automate the process of defect detection based on different metrics. The signal energy is a vital metric that can be utilised to show whether defects exist in composite specimens. In this paper, a dataset is analysed into features and it gets proliferated using a synthesis method based on differential privacy. Thereafter, the dataset is fed into two advanced machine learning algorithms, namely the Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. The results of the CNN show the test Mean-Squared Error (MSE) and Training MSE, which are calculated, showing satisfactory results for the defect and energy, respectively. Moreover, the energy values can be quite accurately predicted using LSTM; the process indicates that defects can be identified in a composite specimen using only the energy of the signal.