Supplementary data for the paper: "An Efficient Pipeline for the Unsupervised Segmentation of Heterogeneous Natural Soundscapes"

Passive Acoustic Monitoring will transform biodiversity assessment and large-scale ecological surveys through continuous ecoacoustic data collection. Yet, dataset growth has outpaced existing segmentation methods, many of which are tuned to narrow taxonomic subsets lacking abiotic sounds, limiting ecological realism. Natural soundscapes contain overlapping biophony, geophony, anthrophony, and technophony, making resource-efficient and ecologically valid segmentation an ongoing challenge. As such, we introduce an unsupervised framework designed to reveal meaningful acoustic structures without predefined labels. The pipeline integrates systematic sampling, sound event detection, Mel-Frequency Cepstral Coefficient extraction, dimensionality reduction via Uniform Manifold Approximation and Projection (UMAP), and clustering with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). We evaluate the framework across six biodiverse Australian soundscapes comprising of (n=19,230) 4.5-second non-overlapping segments. Results indicate that clusters are internally coherent, with Voronoi tessellations over the UMAP space showing distinct spatial boundaries. External validation with (n=2,000) manually annotated samples demonstrates strong alignment with ecologically meaningful sound types. F1-scores ranged from 83.7% to 98.3%, with precision and recall exceeding 91% across most sites. By combining unsupervised clustering with ecological validation, our framework offers a practical, generalisable solution for organising unlabelled ecoacoustic data while reducing manual effort and preserving ecological integrity.