The detection of COVID-19 is and will remain in the foreseeable future a crucial challenge, making the development of tools for the task important. One possible approach, on the confines of speech and audio processing, is detecting potential COVID19 cases based on cough sounds. We propose a simple, yet robust method based on the well-known ComParE 2016 feature set, and two classical machine learning models, namely Random Forests, and Support Vector Machines (SVMs). Furthermore, we combine the two methods, by calculating the weighted average of their predictions. Our results in the DiCOVA challenge show that this simple approach leads to a robust solution while producing competitive results. Based on the Area Under the Receiver Operating Characteristic Curve (AUC ROC) score, both classical machine learning methods we applied markedly outperform the baseline provided by the challenge organisers. Moreover, their combination attains an AUC ROC score of 85.21, positioning us at fourth place on the leaderboard (where the second team attained a similar, 85.43 score). Here, we would describe this system in more detail, and analyse the resulting models, drawing conclusions, and determining future work directions.

Conventional bridge inspection is usually performed by experienced engineers, trying to detect and document damage patterns manually. By increased number of built Bridges, there is a growing interest in automated damage detection methods. Therefore, the field of autonomous bridge inspection with the application of machine learning techniques on UAV-taken images is gaining popularity. Due to recent technological advancement, a large number of datasets can be collected, with a high rate of productivity and accuracy, to train convolutional neural networks (CNNs) leading us to automated Structural health monitoring (SHM). In this paper, a case study is chosen to scan two times with almost one year as a time interval. In the first scanning, dataset was gathered to train four different CNNs. Then, the performance of CNNs was compared for the purpose of autonomous crack detection in the second round of scanning. Models evaluated on a number of performance metrics, namely- (i) accuracy, (ii) loss, (iii) computation time, (iv) model size, and (v) architectural depth. Finally, the performance of studied CNNs is discussed, which can lead researchers in the Transfer-Learning approach to generate a model for damage detection with a limited number of datasets prepared in the first turn of bridge inspection.