Structure Across Voices: Comparing acoustic-event type accumulation and sequence dependence across four vocal repertoires using frozen audio encoders

Sperm-whale codas are conventionally characterized by click count and inter-click intervals (ICIs), leaving recurring differences in constituent click waveforms unresolved. This study tests whether acoustic organization is nested across two scales: within codas, where recurring click-waveform differences may complement ICI timing, and across codas, where recurring whole-coda forms may themselves carry sequence dependence. Candidate recurring click and whole-coda groupings were identified from 1,483 codas without prespecifying waveform categories, then evaluated with native-rate spectral/envelope measurements, exact nuisance matching, held-out timing contrasts, and sequence controls. At the first tier, recurring click-waveform groups differed in spectral slope, bandwidth, flatness, high/low-band energy, and envelope structure within matched date, social unit, individual, and sample-rate strata. Their composition added information about whole-coda grouping beyond timing, while timing remained informative when click composition was fixed. The richer description also carried held-out social-unit-associated information beyond timing. At the second tier, direct native-rate waveform summaries recovered the recurring whole-coda forms well above context-preserving nulls, whereas conventional timing did not; the forms also cross-cut published timing-defined coda types. The preceding two-coda context then added held-out predictive information beyond the immediately preceding coda, while a third preceding coda provided no reliable further gain. Together, these results support two-tier acoustic organization: recurring waveform differences and ICI timing jointly organize individual codas, and acoustically grounded whole-coda forms in turn show bounded second-order predictive dependence across sequences.

The vocal music of each language carries a distinctive sonic identity, even without instrumental accompaniment. We ask whether these differences are measurable and traceable to specific phonemes. To tackle this question, we introduce phoneme-conditional analysis, which isolates the acoustic effect of typologically distinctive phonemes by comparing marker syllables against matched non-marker controls within the same song, holding singer, melody, and genre constant. Across nine typologically diverse languages and thousands of songs, we measure effects along five acoustic dimensions. Song-level profiles built from these effects identify the language of an unaccompanied vocal at 85.5% balanced accuracy in a nine-way classification with folds grouped by artist; whether the separability arises by accumulation of the phoneme-local effects themselves is left open. Our findings suggest that phonological structure leaves systematic and measurable traces in how each language is sung.

Do learned audio embeddings encode structure that nobody told them to encode? We probe four large pretrained audio models (AST, CLAP, BEATs-bio and BirdNET) with a downstream task none of them saw during training: recovering phylogenetic distance from species vocalizations. If the geometry of the embedding space tracks the tree of life, the representation is picking up something deeper than the labels the model was optimized for. We run Mantel tests across two independent radiations. In 32 marine mammal species (1,754 recordings from the Watkins Marine Mammal Sound Database) the foundation models recover strong phylogenetic signal within the 26 cetaceans (CLAP r=0.82, BEATs-bio r=0.82, AST r=0.74; all p<0.001), among the highest acoustic-phylogenetic correlations reported for any taxon. Hand-crafted MFCC features (105d) find nothing (r=0.040, p=0.338). The gap survives after PCA-projecting every embedding down to 105 dimensions, so it is not an artefact of representation size. It also survives a partial Mantel test controlling for dominant frequency (partial Mantel r=0.404, keeping 97% of the variance explained), so it is not just pitch in disguise. We repeat the analysis on 20 bird species using the Jetz et al. (2012) phylogeny, and this time add BirdNET, a classifier trained end-to-end on around 6,000 bird species. The general-purpose foundation models recover the signal again (AST r=0.55, CLAP r=0.52). The unexpected result is that neither BirdNET nor the bioacoustic BEATs-bio beat them (r around 0.32 to 0.36). Matching the training domain to the target taxon does not, by itself, help. Pretrained audio embeddings carry evolutionary information across two independent radiations, and domain-specific pretraining is not required for it to emerge.