Musical instrument recognition enables applications such as instrument-based music search and audio manipulation, which are highly sought-after processes in everyday music consumption and production. Despite continuous progresses, advances in automatic musical instrument recognition is hindered by the lack of large, diverse and publicly available annotated datasets. As studies have shown, there is potential to scale up music data annotation processes through crowdsourcing. However, it is still unclear the extent to which untrained crowdworkers can effectively detect when a musical instrument is active in an audio excerpt. In this study, we explore the performance of nonexperts on online crowdsourcing platforms, to detect temporal activity of instruments on audio extracts of selected genres. We study the factors that can affect their performance, while we also analyse user characteristics that could predict their performance. Our results bring further insights into the general crowd's capabilities to detect instruments.

@en

Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this study, we examine the performance of Convolutional Neural Networks, on four different datasets of side- channel data and we compare our models with conventional Machine Learning algorithms and a CNN model from literature. We found that CNNs have the potential to achieve high accuracy performance (99.8%), although their capacity is heavily influenced by the use case. We also found that certain Machine Learning algorithms can outperform CNNs in certain cases, leaving an open debate on the performance gains of the latter.