Effects of interpulse-interval variation on deep-learning classification of bat vocalizations

A study posted to arXiv (2610.02284v1) found limited support for the hypothesis that natural interpulse-interval (IPI) variation contributes substantially to automated bat-species classification, with transformer model PaSST reaching 71 ± 2.3% accuracy under natural IPI versus 70 ± 6.3% under normalized 50-ms IPI, while EfficientNet-B0 rose from 47 ± 4.7% to 57 ± 3.9%. In cross-condition tests, models trained on natural-IPI recordings outperformed those trained on normalized-IPI recordings on the natural-IPI test set, with accuracy falling from 54% to 50% for EfficientNet and from 65% to 57% for PaSST. The pretrained classifiers BatDetect2 and BAT differed little between IPI conditions, and the authors conclude results obtained under normalized conditions may not transfer fully to natural recordings.

arXiv:2610.02284v1 Announce Type: new Abstract: 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.