Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls
Fig 5
Projections of bonobo calls into bidimensional acoustic spaces through S-UMAP computed on the raw acoustic features of the Bioacoustic, DCT, and MFCC sets (1,560 calls; each dot = 1 call; different colors encode different hand-labeled categories).
Left. Top. S-UMAP projection supervised by call types. Bottom. Silhouette profiles corresponding to the call type clustering, built from a 100-repetition distribution of silhouette scores, with averages and standard deviations per call type being represented by dashed vertical and horizontal lines, respectively. Right. Top. S-UMAP projection supervised by individual identities. Bottom. Silhouette profiles corresponding to the individual signature clustering, built from a 100-repetition distribution of silhouette scores, with averages and standard deviations per individual being represented by dashed vertical and horizontal lines, respectively.