Unsupervised Environmental Sound Classification Based On Topological Persistence

Environmental sound classification (ESC) is a significant problem in signal processing and machine learning. In this paper, a novel unsupervised learning approach is proposed using topological data analysis. Each sound signal is transformed into a point cloud by time-delay embedding. The persistence diagrams are obtained after computing the persistent homology of Vietoris-Rips filtrations. The sound signals are assigned to different classes using kmeans algorithm based on the bottleneck distance matrix of persistence diagrams.