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Automated Capuchinbird Call Detection with CNN Based Audio Classifier

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Poonam Kadian[1] , Anshul Pareek [2] , Shaifali M.Arora[3]
» doi: 10.48047/ecb/2023.12.Si13.167


Bird density prediction plays an important role in monitoring and further protecting biodiversity. Recent advances in acoustic sensor networks and deep learning techniques provide a novel way for continuously monitoring birds. In this project we aim at devising a method to measure the density of capuchin birds, natively found in rainforests of Amazon. We will be using forest audio clips to count the capuchin bird calls, thereby predicting the Capuchin bird density inthe forest. To count the Capuchin bird calls, we will be using a CNN (Convolutional Neural Network) model which feeds on audio spectrogram to recognize Capuchin bird calls. The major libraries required to build and train our model are tensorflow, numpy array and keras

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