DCUSV: Deep Clustering of Ultrasonic Vocalizations in Rodents

Analyzing ultrasonic vocalizations (USVs) is critical for understanding rodents’ emotional states and social behaviors. This work presents Deep Clustering of USVs (DCUSV), an automated deep clustering pipeline for analyzing preprocessed USV contours that addresses key challenges in effectively clustering USVs and revealing distinct patterns in rodent vocal behavior. DCUSV employs a dense autoencoder to compress high-dimensional spectrograms into a latent space suitable for clustering, followed by a combination of Uniform Manifold Approximation and Projection (UMAP), Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and Agglomerative clustering, with hyperparameter optimization, to group USVs based on their spectro-temporal features. Clustering is evaluated using the Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index. In addition, Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and UMAP are used to visualize and analyze the clustering results. Performance benchmarks revealed that DCUSV achieves 1.50× higher Silhouette score than K-Means and Deep Embedded Clustering (DEC), and 1.29× higher Silhouette score than HDBSCAN; 3.07×, 3.11×, and 29.15× higher Calinski-Harabasz scores than K-Means, DEC, and HDBSCAN, respectively; and 2.28×, 2.31×, and 1.92× lower Davies-Bouldin scores (lower is better) than K-Means, DEC, and HDBSCAN, respectively. Applying DCUSV revealed four distinct call-type families, closely aligning with manually defined categories without requiring manual grouping. Furthermore, DCUSV identified statistically significant shifts in call-type distributions across experimental conditions, demonstrating its ability to capture behaviorally meaningful changes in vocal expression. Thus, DCUSV enables robust analysis of USV structure and uncovers novel patterns in rodent vocal behavior.