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Open AccessArticle

Categorization of Mouse Ultrasonic Vocalizations Using Machine Learning Techniques

1
Department of Music Technology and Acoustics, Hellenic Mediterranean University, 71500 Crete, Greece
2
Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus
3
Department of Biology, University of Crete, 70013 Heraklion, Greece
*
Author to whom correspondence should be addressed.
Acoustics 2019, 1(4), 837-846; https://doi.org/10.3390/acoustics1040050
Received: 13 July 2019 / Revised: 21 October 2019 / Accepted: 23 October 2019 / Published: 4 November 2019
A study of the ultrasonic vocalizations of several adult male BALB/c mice in the presence of a female, is undertaken in this study. A total of 179 distinct ultrasonic syllables referred to as “phonemes” are isolated, and in the resulting dataset, k-means and agglomerative clustering algorithms are implemented to group the ultrasonic vocalizations into clusters based on features extracted from their pitch contours. In order to find the optimal number of clusters, the elbow method was used, and nine distinct categories were obtained. Results when the k-means method was applied are presented through a matching matrix, while clustering results when the agglomerative technique was applied are presented as a dendrogram. The results of both methods are in line with the manual annotations made by the authors, as well as with the ones presented in the literature. The two methods of unsupervised analysis applied on 14 element feature vectors provide evidence that vocalizations can be grouped into nine clusters, which translates into the claim that there is a distinct repertoire of “syllables” or “phonemes”. View Full-Text
Keywords: ultrasonic vocalizations; mice BALB/c biosignals; k-means clustering; bioacoustics ultrasonic vocalizations; mice BALB/c biosignals; k-means clustering; bioacoustics
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MDPI and ACS Style

Kouzoupis, S.; Neocleous, A.; Athanassakis, I. Categorization of Mouse Ultrasonic Vocalizations Using Machine Learning Techniques. Acoustics 2019, 1, 837-846.

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