Vibroscape is a naturally occurring vibrational environment composed of biological, geophysical, and anthropogenic vibrations. In this still largely unexplored realm of the vibrational landscape, our primary focus is on the vibrational communication of insects. Therefore, as part of our master's thesis, we examined the ability to detect insect calls in vibrospace recordings using various machine learning models. We compared the performance of manual features such as LFCC, MFCC, and the combination of both as hybrid features to the performance of deep features extracted using openl3. We compared the effectiveness of different simple machine learning models, such as the SVC model, with directed neural networks. We also employed deep CNN and TFNet models without pre-computed features, with PCEN normalization and data augmentation techniques like mixup and specAugment. For the detection of all insect calls, the best results were obtained using a combination of openl3 features and the SVC model, achieving an F1 score of 79.950% on the test data.
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