Extended Data Fig. 1: Clustering of juvenile and adult zebra finch song.

a, Vocal development in male zebra finches. Tutoring by an adult male started at around day 46 (post-hatch) and lasted 10–20 days. b, Time course of the acoustic feature frequency modulation (FM), for syllable b in the example bird (compare with Fig. 1b). c, Normalized mean silhouette values for 2–10 clusters for vocalizations from the seven days shown in d. High values indicate evidence for the respective cluster count. Normalized mean silhouette coefficients are based on 20 repetitions of k-means clustering of random subsets of 1,000 68-ms onset-aligned spectrogram segments from a single day (as in d), projected onto the first five principal components. d, t-SNE visualizations of vocalizations produced on a given day post-hatch for the example bird (bird 4, the same bird as in Fig. 1a–c). A separate embedding was computed for each day, and the embedding’s initial condition was based on the previous day. Note the gradual emergence of clusters, each corresponding to a distinct syllable type (for example, syllables i, a, b, c in Fig. 2a). e, Average fraction of neighbours from a different cluster, as a function of neighbourhood size. These data are analogous to those from c, d but for vocalizations from day 90 (12,854 data points), when clusters are fully developed. For a wide range of neighbourhood sizes, the neighbours of a data point mostly belong to the same cluster or syllable type. For a neighbourhood size of 100, the average fraction of out-of-cluster neighbours from the same day is 0.0089. Thus, for an appropriately chosen neighbourhood size, nearest-neighbour methods respect clustering structure in the data by construction, and sidestep having to explicitly identify clusters in the data. In most analyses, we computed nearest neighbours for data from all days, meaning that clustering structure is respected even for neighbourhood sizes that are slightly larger than those suggested in e.