Visual and Acoustic Identification of Bird Species (original) (raw)
This paper combines both approaches for bird 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.
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