@conference {57652, title = {Automatic Segmentation of Ultrasonic Vocalizations in Rodents}, year = {2019}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Ultrasonic vocalizations studies in rodents have increasingly drawn researchers attention as it have been considered a powerful tool to understand the animals behavior and their interactions in different social and environmental contexts.

This paper presents an entropy-based algorithm for accurate and robust segmentation of mouse ultrasonic calls. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the audio segments accurately. The new approach for mice calls detection has been able to detect up to 97\% of the vocalizations.

}, keywords = {Rodents ultrasonic vocalizations, Signal processing, Signal segmentation, Spectral Entropy}, isbn = {978-3-030-31634-1}, doi = {10.1007/978-3-030-31635-810.1007/978-3-030-31635-8_5}, url = {http://link.springer.com/10.1007/978-3-030-31635-8}, author = {Pessoa, Diogo and Petrella, Lorena and Castelo-Branco, Miguel and Teixeira, C{\'e}sar}, editor = {Henriques, Jorge and Neves, Nuno and de Carvalho, Paulo} } @conference {57418, title = {Applying convolutional neural networks to the analysis of mouse ultrasonic vocalizations}, year = {2019}, abstract = {

Many species in diverse taxonomic groups, including rodents, bats, and insects, communicate with complex ultrasonic vocalizations (USVs) (\>20 kHz). Two main components of processing and analyzing USV recordings include detection and classification of syllable types. Recently we developed an efficient algorithm for detecting mouse USVs (Automatic Mouse Ultrasound Detector (A-MUD)). The main challenge is detecting USVs under conditions with a low signal-to-noise ratio, while minimizing rates of false positives (FP). Mice produce many short USVs (\< 10 millisecond) that inflate FPs. We aimed to improve the detection of mouse USVs with A-MUD by classifying vocalizations into three discrete syllable types (with 0, 1, or \≥2 frequency-jumps) or FP. Supervised Convolutional Neural Networks (CNNs) were fed by 2D Gammatone Spectrograms (GSs) adapted to the frequency range of mice. Evaluation of performance shows that CNNs yielded an overall accuracy of 95\±1.2\% and macro-F1 score of 90\±2.7\%. In contrast, multilayer feed-forward neural networks fed by vectorized spectrograms provided an overall accuracy of only 85.4\±1.9\% and macro-F1 score of 75.4\±2.9\%, and therefore, the chosen CNNs outperformed this conventional classification method.

}, keywords = {Deep learning, Image-based classification, Ultrasonic vocalizations (USVs)}, author = {Reyhaneh Abbasi and Peter Balazs and Anton NOLL and Doris NICOLAKIS and Maria Adelaide Marconi and Sarah M. Zala and Dustin J. Penn} }