In this paper, we present graph neural stochastic diffusion (GNSD), a novel framework for estimating predictive uncertainty on graphs by establishing theoretical connections between GNNs and stochastic partial differential equation.

Sound-based Bird Classification - using AI, acoustics and ornithology to classify birds in the environment, an environmental awareness project (Web Application, Flask, Python)

In this paper, we propose a gradient-tracking based nonconvex stochastic decentralized (GNSD) algorithm for solving nonconvex optimization problems, where the data is partitioned into multiple parts and processed by the local computational resource.

2021年3月1日 · Recent advances in deep artificial neural networks (DNNs) have transformed the field of machine learning, frequently outperforming traditional signal processing techniques in the domain of acoustic event detection and classification. We developed a DNN, called BirdNET, capable of identifying 984 North American and European bird species by sound.

This research paper presents an innovative approach for bird species detection using Convolutional Neural Networks (CNN) and the EfficientNet-B0 architecture. The study focuses on the application of deep learning techniques to address the challenging task of automatically identifying bird species from images.

This repository contains a deep learning project focused on classifying 525 bird species using convolutional neural networks (CNNs). The dataset comprises 84,635 training images, 2,625 test images, and 2,625 validation images, with each image having dimensions of 224 x 224 x 3.

2023年9月14日 · Our constructed bird detection dataset of GBDD1433-2023, includes 1433 globally common bird species and 148,000 manually annotated bird images. Based on this dataset, two-stage detection models like Faster R-CNN and Cascade R-CNN demonstrated superior performances, achieving a Mean Average Precision (mAP) of …

2021年4月16日 · Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem.

2024年12月10日 · Engineered a robust deep learning model using Convolutional Neural Networks and TensorFlow to classify 114 bird species based on audio recordings. Model achieved an impressive accuracy of 93.4%, providing valuable insights for conservationists and ecologists in the wildlife & ecological research sectors.

2019年12月4日 · In this study, we compare different classification models and selectively fuse them to further improve bird sound classification performance. Specifically, we not only use the same deep learning architecture with different inputs but also employ two different deep learning architectures for constructing the fused model.