Many odontocetes produce frequency modulated tonal calls known as whistles. The ability to automatically determine time × frequency tracks corresponding to these vocalizations has numerous applications including species description, identification, and density estimation. This work develops and compares two algorithms on a common corpus of nearly one hour of data collected in the Southern California Bight and at Palmyra Atoll. The corpus contains over 3000 whistles from bottlenose dolphins, long- and short-beaked common dolphins, spinner dolphins, and melon-headed whales that have been annotated by a human, and released to the Moby Sound archive. Both algorithms use a common signal processing front end to determine time × frequency peaks from a spectrogram. In the first method, a particle filter performs Bayesian filtering, estimating the contour from the noisy spectral peaks. The second method uses an adaptive polynomial prediction to connect peaks into a graph, merging graphs when they cross. Whistle contours are extracted from graphs using information from both sides of crossings. The particle filter was able to retrieve 71.5% (recall) of the human annotated tonals with 60.8% of the detections being valid (precision). The graph algorithm's recall rate was 80.0% with a precision of 76.9%.
Culture, a pillar of the remarkable ecological success of humans, is increasingly recognized as a powerful force structuring nonhuman animal populations. A key gap between these two types of culture is quantitative evidence of symbolic markers—seemingly arbitrary traits that function as reliable indicators of cultural group membership to conspecifics. Using acoustic data collected from 23 Pacific Ocean locations, we provide quantitative evidence that certain sperm whale acoustic signals exhibit spatial patterns consistent with a symbolic marker function. Culture segments sperm whale populations into behaviorally distinct clans, which are defined based on dialects of stereotyped click patterns (codas). We classified 23,429 codas into types using contaminated mixture models and hierarchically clustered coda repertoires into seven clans based on similarities in coda usage; then we evaluated whether coda usage varied with geographic distance within clans or with spatial overlap between clans. Similarities in within-clan usage of both “identity codas” (coda types diagnostic of clan identity) and “nonidentity codas” (coda types used by multiple clans) decrease as space between repertoire recording locations increases. However, between-clan similarity in identity, but not nonidentity, coda usage decreases as clan spatial overlap increases. This matches expectations if sympatry is related to a measurable pressure to diversify to make cultural divisions sharper, thereby providing evidence that identity codas function as symbolic markers of clan identity. Our study provides quantitative evidence of arbitrary traits, resembling human ethnic markers, conveying cultural identity outside of humans, and highlights remarkable similarities in the distributions of human ethnolinguistic groups and sperm whale clans.
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