AI-Powered Classification of Small Mammal Vocalizations

NIAID Data Ecosystem2026-05-02 收录

生物声学分类

小型哺乳动物识别

数据链接：

https://zenodo.org/record/15183031 数据链接链接失效反馈

官方服务：

资源简介：

Raw data to my master's thesis "AI-Powered Classification of Small Mammal Vocalizations", containg: 1052 family-specific small mammal vocalization (Cricetidae, Muridae, Soricidae) collected in the Black Forest, Germany. a customn BirdNET model fine-tuned on the classification of small mammal vocalizations

创建时间：

2025-04-09

Dataset of Bottlenose and Common Dolphins Acoustic Recordings in the English Channel

海豚声学信号分析

生物声学分类

We provide dolphin whistles from two species: bottlenose dolphins (Tursiops truncatus) and short-beaked common dolphins (Delphinus delphis). Data are available through CSV files containing the time–fr

Zenodo2026-04-15 更新60

InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)

昆虫声学识别

生物声学分类

Updated full version with training, validation and test sets. Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-bas

NIAID Data Ecosystem40

Small mammal classification model

相机陷阱图像分析

小型哺乳动物识别

Classification model for small mammals and images used for training, validating and testing the model. The script used for training and further information are available on https://github.com/hannaboe

Zenodo2023-04-05 更新20

Bird_sound_audio_and_annotation_files

鸟类声学监测

生物声学分类

This is a dataset of bird sound collected at the Intaka Island nature reserve.

NIAID Data Ecosystem50

Mean and standard deviation of classifier accuracy across repeated training instances using each the three machine learning methods (compound index, pretrained CNN and trained CNN) at six different tasks. Accuracy is the proportion of one-minute recordings from the test data that were correctly classified. Methods where accuracy was reported as significantly higher by the ANOVA test are indicated in superscript next to the mean value for the respective method (A = highest group, B = second highest group, no letter = lowest group). The Random baseline accuracy indicates the expected accuracy of a model that performs random classification. N = 100 for all tasks, except the Fish diversity (Australia) and Depth (French Polynesia) tasks, where N = 32.

生物声学分类

机器学习模型评估

NIAID Data Ecosystem20