Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.

Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data [ICVGIPW'18]

Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.

Code for searching the www.xeno-canto.org bird sound database, and training a machine learning model to classify birds according to their sounds.

Explores jigsaw puzzles solvinig as pre-text task for fine grained classification for bird species identification (Implemented with pyTorch)

A repo designed to convert audio-based "weak" labels to "strong" intraclip labels. Provides a pipeline to compare automated moment-to-moment labels to human labels. Methods range from DSP based foreground-background separation, cross-correlation based template matching, as well as bird presence sound event detection deep learning models!

BirdNET as a systemd service with other features.

ResNet-34 Model trained from scratch to classify 450 different species of birds with 98.6% accuracy.

Classifies a bird's species using a neural network in tensorflow..

Computer vision website which recognizes and provides information about birds in user-uploaded photos.

Source code for BMBF InnoTruck demo of BirdNET.

Fine-grained species classification

Bird Classifier developped in tensorflow using pre-trained model from Tensorflow Hub and running on Google Colab

Code used for my final project in Computer Vision at Texas State University, Spring 2019

Southern African Bird Call Audio Identification Challenge

MVA - Kaggle Challenge - Bird Image Recognition

Signature Work @ DKU: Large Scale Bird Sound Recognition in China Region

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.

Determine the 🐦 from its 🎵

Bird Sound Recognize