Automated Bird Species Identification using Neural Network
Budget: ₹1,500 – ₹12,500 INR
The deep learning models are trained on a dataset of bird sounds to learn the patterns and characteristics of different bird species. The dataset can be sourced from publicly available sources or collected by the developers of the system.
The proposed system uses deep learning models ResNet50, Xception, and InceptionV3 for bird audio classification. These models have been shown to achieve high accuracy on image classification tasks and can be adapted for audio classification.
The system is designed to be accessible through a mobile app. This means that users can record bird sounds using their phone's microphone and get real-time feedback on the bird species.
The methodology for bird audio classification involved training a CNN model using three popular architectures, namely
ResNet,
Inception, and
Xception,
to extract features from the pre-processed audio signals and predict the probability distribution of bird species.
The proposed system uses deep learning models ResNet50, Xception, and InceptionV3 for bird audio classification. These models have been shown to achieve high accuracy on image classification tasks and can be adapted for audio classification.
The system is designed to be accessible through a mobile app. This means that users can record bird sounds using their phone's microphone and get real-time feedback on the bird species.
The methodology for bird audio classification involved training a CNN model using three popular architectures, namely
ResNet,
Inception, and
Xception,
to extract features from the pre-processed audio signals and predict the probability distribution of bird species.