Bounding-box detection data for delphinid whistles

Deep learning methods offer automated solutions for detecting marine mammal calls, yet require time-intensive development for optimized neural network performance, including carefully curating data and creating a robust network architecture. Using data collected in two aquariums and two open ocean environments, we evaluated the performance of a series of pre-trained object detection networks, CSP-DarkNet-53, ResNet-50, and Tiny YOLO, in detecting highly variable bottlenose dolphin (Tursiops truncatus) whistles using DeepAcoustics, a user-friendly deep learning tool. We compared the F1-score, average precision (AP), and mean AP performance of all network architectures with combinations of training samples from each acoustic environment. CSP-DarkNet-53 consistently outperformed Tiny YOLO and ResNet-50 across various test datasets, demonstrating robustness, but underperformed in select scenarios. Performance remained higher for aquarium data compared to open ocean data based on AP and mean..., , , # Bounding-box detection data for delphinid whistles

Dataset DOI: [https://doi.org/10.5061/dryad.z34tmpgq6](https://doi.org/10.5061/dryad.z34tmpgq6)

## Description of the data and file structure

This dataset supports the study titled *\"Effects of network selection and acoustic environment on bounding-box object detection of delphinid whistles using a deep learning tool.\"* It includes audio recordings, annotations, trained models, and evaluation metrics used to assess the performance of deep learning networks for detecting bottlenose dolphin (*Tursiops truncatus*) whistles.

The dataset is organized into three folders:

* **`Training_Audio_and_Anntoations`.zip**: Contains training audio files and corresponding annotation data (e.g., selection tables and detection labels) from four acoustic environments—two aquarium and two open ocean settings. Subfolders by dataset house the audio files, selection tables (.txt), and DeepAcoustics imported .mat file. The aquarium datasets contain two m...,