We explore the effectiveness of spectrographic cross-correlation (SPCC) combined with principal coordinates (PC0) analysis as a method for sound comparison. We do this using synthetic sounds modeled after the individually-distinctive, harmonically-rich contact calls of wild orange-fronted conures Aratinga canicularis. Calls with acoustic properties similar to Aratinga contact calls are common in other taxa including non-oscine birds, primates and cetaceans. We generated signals with known variations in time-frequency pattern, duration, noise level, harmonic content and harmonic weighting, and applied SPCC-PCO analysis to obtain an ordering of sounds in n-dimensional space. We find that shared time-frequency patterns dominate the positioning of sounds in PCO space. This was true despite high variability in signal-to-noise ratio (from -60 to +40 dB) and duration (150-275 ms). Furthermore, inclusion of naturally-weighted harmonics (versus fundamentals only) enhances, rather than obscures, the separation of call types. We conclude that SPCC-PCO is an effective method for sorting sounds based on overall time-frequency pattern. In addition, the resulting PCO measures can be used in statistical tests of association with extrinsic variables. The method is thus an effective starting point for examining most bioacoustic hypotheses.
spectrographic cross-correlation, principal coordinates analysis, sound comparison, parrot vocalizations, sound synthesis