Abstract

Mice vocalize in the ultrasonic range during social interactions. These vocalizations are used in neuroscience and clinical studies to tap into complex behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has been traditionally a manual process, which is prone to errors and human bias, and is not scalable to large scale analysis. We propose a new method to automatically create a dictionary of USVs based on a two-step spectral clustering approach, where we split the set of USVs into inlier and outlier data sets. This approach is motivated by the known degrading performance of sparse subspace clustering with outliers. We apply spectral clustering to the inlier data set and later find the clusters for the outliers. We propose quantitative and qualitative performance measures to evaluate our method in this setting, where there is no ground truth. Our approach outperforms two baselines based on k …

Date

May 4, 2020

Authors

Jiaxi Wang, Karel Mundnich, Allison T Knoll, Pat Levitt, Shrikanth Narayanan

Conference

ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

3432-3436

Publisher

IEEE