International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Visual and Acoustic Identification of Bird Species Dhiraj Patil1, Rutwi Bodhe2, Rupali Pawar3, Tanisha Doshi, Vidhya Vasekar Department of Computer Engineering, Sinhgad Institute of Technology and Science, Narhe, Pune 411041. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper combines both approaches for bird many of the remaining birds across the islands are isolated
in difficult-to-access, high-elevation habitats[8]. With physical monitoring difficult, scientists have turned to automatic image and sound recordings. Known as bioacoustic monitoring or bird watching, this approach could provide passive, low labour, and cost-effective strategy for studying endangered bird populations. Approaches like these have interesting correct classification rates which range between 78 - 95%, which depends upon the number of bird species that have been tested.
species identification by extracting visual features from bird images and acoustic features from bird calls. Some bird species are rarely found in certain regions, and it's difficult to track them if done the prediction is difficult. In order to withstand this issue, we've come across a significant and easier way to recognize these bird species based on their features. We've used BirdCLEF 2022 dataset for the audio segment and the BIRDS 400 dataset for the image segment for the training and testing parts. Since among most of the approaches, we have studied CNN as vanquishing, therefore we've used CNN for both visual as well as acoustic identification. CNN is the strong assemblage of ML which has proven efficient in image processing. Our project has become attractive because of the techniques and recent advances within the domain of deep learning. With novel preprocessing and data augmentation methods, we train a convolutional neural network on the largest public obtainable dataset. By establishing a dataset and using the rule of similarity comparison algorithms, our system can provide the best results. By using our system, everyone will simply be able to determine the species of the particular bird which they provide image/audio or both as input.
Prediction of bird species, to which category they belong by using image or audio data is known as bird species identification. The recognition of bird species can be possible through audio or image. The use of automated methods for bird identification is an effective way to assess the quantity and diversity of birds that appear in a region. Identification is a challenging problem both for humans as well as for computational algorithms that focus to do this task automatically. Looking complexity of the problem, a scenario which is visually and acoustically limited, a very high number of classes, high visually and acoustically similar bird species, background noise and a high variety of the acquisition conditions. Novel methods are required to provide more reliable and accurate results than those achieved till now by both visual and acoustic approaches. The problem can be solved by training a mechanism which can make predictions which can be similar to test samples which are prior available.
Key Words: Bird Species, CNN, ML
1. INTRODUCTION In total there are 9000 bird species in our world. As the “extinction capital of the world,” Hawai'i has lost 68% of its bird species, the results of which might harm entire food chains[7]. Bird species identification arouses interest in different groups of admirers and experts whether through the beauty of birds and their sound or by their ecological importance. When collaborating with the latest large availability of automated recording units it becomes prominent why remote, systematic and non-intrusive, acoustic biodiversity surveys are getting popular in the past decade. Acoustically active biota groups and their specific information can be obtained by acoustic monitoring, and an index of biodiversity can be generated based on how complex the calls recorded within a region are.
Providing a solution to this problem, this system has been proposed that uses both .jpg & .wav files to predict bird species from data users put as input. 1.1 PROBLEM DEFINITION: Identifying a bird can be a challenge, even for experienced birders. If you're new in using field guides, it can be difficult to figure out how & were to even start searching in the 100’s of pages of bird species. By some features like size, shape and colour birds can be classified. We can classify species of birds using CNN.
Population monitoring is used by researchers to understand how native birds react to changes in the environment and conservation efforts. Several real-world applications can rely on birds such as monitoring of environmental pollution, assessing the quality of the environment and estimating sustainability indicators. But
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1.2 OBJECTIVES ●
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To increase the correct classification rate & decrease rejection rates, even while the number of bird species gradually increases using CNN.
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