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

arXiv: https://arxiv.org/abs/2610.02284 · HTML: https://mlsanity.com/paper/2610.02284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 2 of 2)

- [Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers](https://mlsanity.com/paper/2211.13956/) (2022)
- [Automatic Bat Call Classification using Transformer Networks](https://mlsanity.com/paper/2309.11218/) (2023)

## Abstract (arXiv)

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 ± 2.3%) and normalized-IPI (70 ± 6.3%) conditions, whereas EfficientNet accuracy increased from 47 ± 4.7% to 57 ± 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.