International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Bird Species Classification based on Images and Audios using Deep Learning Jahnavi S, Keerthana K, Priyanka S, Sanjana L, Dr. Shilpa B L Dept. of Information Science and Engineering (of VTU Affiliation), GSSS Institute of Engineering and Technology for Women, Mysuru, India. --------------------------------------------------------------------------***--------------------------------------------------------------------------bird species distributions, behaviours, and interactions Abstract – Birds play a crucial part in our is essential for analysing conservation strategies and safeguarding biodiversity. Traditional methods of bird classification predominantly rely on manual observation, which is labor- intensive, time-consuming, and prone to human error. In our model, we address the challenges of limited labelled data by employing data augmentation strategies such as cropping, rotation and flipping, which help to augment the training dataset, thereby reducing overfitting and improving the generalization ability of the model.
ecosystem. Accurate bird classification is essential for understanding ecosystem dynamics, assessing biodiversity, and guiding conservation strategies. Traditional methods relying on manual observation are time-consuming and limited in scope, hindering comprehensive ecological studies and conservation efforts. In this paper, we propose a Convolutional Neural Network (CNN) approach for the automated classification of bird species from images or audio. The visual component of our model uses VGG16 (a specific CNN architecture) to extract features from bird images, while the Multilayer Perceptron (MLP) classifier extracts audio features. Our methodology leverages the power of deep learning to recognize distinct features of bird species, thus enabling accurate identification. We employ a dataset comprising images of various bird species. The dataset is pre-processed to eliminate noise and enhance image and audio quality, thereby facilitating effective training of the CNN model. The architecture is designed to extract hierarchical features from input images and audio and learn discriminative patterns associated with different bird species. Experimental results demonstrate the effectiveness of our proposed approach in accurately identifying the bird species. The model’s performance is evaluated using standard metrics such as accuracy. The proposed model promises potential for applications in ecosystem monitoring and environmental conservation.
Deep learning implemented in our model, has revolutionized various fields, including computer vision and audio processing, by enabling machines to automatically learn hierarchical representations from raw Data. In recent years, Convolutional Neural Networks (CNN) have demonstrated remarkable performance in image classification tasks, while Multilayer Perceptron (MLP) has shown promising results in audio-related tasks such as speech recognition and sound classification. A. Motivation The motivation behind this research is driven by the need for efficient and accurate methods to monitor avian populations and their habitats. Accurate identification of bird species is crucial for assessing the population, habitats, and other details of birds. The endeavour to classify bird species through images and audio using deep learning is fueled by the need for effective, scalable methods for monitoring avian populations and ecosystems. Our methodology focuses on designing an efficient CNN architecture capable of capturing fine-grained features of different bird species.
Keywords- bird species, species detection, deep
learning, Convolutional Neural Network (CNN), VGG16, image classification, audio classification, ecosystem monitoring, biodiversity conservation.
B. Problem Statement
1. INTRODUCTION
The traditional methods of bird classification rely on manual observation, that are often time-consuming and reliant on expert knowledge, posing limitations in scale and accuracy. These challenges enforce the need for automated approaches for species detection and classification of birds. By leveraging deep learning techniques, the goal is to overcome the limitations of traditional methods.
As integral components of ecosystems, birds play multifaceted roles, including seed dispersal, pollination, and insect control, thereby exerting profound influences on ecosystem dynamics and functioning. Furthermore, birds serve as indicators of environmental health, with changes in bird populations often reflecting broader ecological shifts. Understanding
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