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Temporal context may aid automated bat-species classification, but the contribution of specific features remains unclear. We investigated whether variation in the interpulse interval (IPI)-the time between consecutive call onsets-provides species-discriminative information and whether transformer-based models are more sensitive to this information than convolutional neural networks. We created two matched datasets from European bat recordings: a natural-IPI condition retaining the original call timing and a normalized-IPI condition in which call onsets were spaced at 50-ms intervals. EfficientNet-B0 and PaSST were fine-tuned and evaluated within each condition. In an additional experiment, each architecture was trained separately on natural-IPI and normalized-IPI recordings, and evaluated on the same natural-IPI test set. Finally, the pretrained classifiers BatDetect2 and BAT were evaluated on both conditions. Within-condition IPI normalization had model-dependent effects. PaSST accuracy differed little between the natural-IPI ($71 \pm 2.3\%$) and normalized-IPI ($70 \pm 6.3\%$) conditions, whereas EfficientNet accuracy increased from $47 \pm 4.7\%$ to $57 \pm 3.9\%$. PaSST exceeded EfficientNet under both conditions. In the cross-condition evaluation, models trained on natural-IPI recordings outperformed those trained on normalized-IPI recordings on the natural-IPI test set: accuracy decreased from 54% to 50% for EfficientNet and from 65% to 57% for PaSST. BatDetect2 and BAT differed little between IPI conditions. Overall, we found limited support for the hypotheses that natural IPI variation contributes substantially to bat-species classification and that it is used more effectively by transformer-based than CNN-based models. Nevertheless, the cross-condition performance decrease shows that results obtained under normalized conditions may not transfer fully to natural recordings.

Vocal communication is a conserved vertebrate social behavior that begins at birth with the cries of infants. These neonatal vocal signals vary across contexts to elicit parental care that matches inf...

Scientific Reports - Dogs’ reactions to motivations and emotions in conspecific and heterospecific vocalizations

Changes in marine animal communities shape ecological processes and ecosystem functioning. Monitoring temporal community dynamics is increasingly important under global change, yet remains challenging...

The workshop aims to provide a venue for researchers working on computational analysis of sound events and scene analysis to present and discuss their results. The 11th Workshop on Detection and Classification of Acoustic Scenes and Events, DCASE 2026, will be held in Boston on 28-29 October. It is co-organized …

Humans organize social interactions in part by referring to others using proper names (hereafter “names”). Names might also facilitate the complex social lives of animals. Several animal species produ...

Tasks We are excited to announce the 2026 BioDCASE task line up! Multi-Channel Alignment Alignment Task 1 Bioacoustic and environmental recordings are often captured using multiple devices operating simultaneously, for example with passive autonomous recorders or animal-borne loggers. When such recordings are not temporally aligned, analyses that rely on relative …