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”.

Time reversal has been demonstrated to be effective for source and novelty detection and localization. We extend here previous work in the case of a coupled structural-acoustic system, to which we refer to as vibro-acoustic. In this case, novelty means a change that the structural system has undergone and which we seek to detect and localize. A single source in the acoustic medium is used to generate the propagating field, and several receivers, both in the acoustic and the structural part, may be used to record the response of the medium to this excitation. This is the forward step. Exploiting time reversibility, the recorded signals are focused back to the original source location during the backward step. For the case of novelty detection, the difference between the field recorded before and after the structural modification is backpropagated. We demonstrate that the performance of the method is improved when the structural components are taken into account during the backward step. The potential of the method for solving inverse problems as they appear in non destructive testing and structural health monitoring applications is illustrated with several numerical examples obtained using a finite element method.