Identification of Bird via Acoustics for Bird Species Population Monitoring and Conservation

Nowadays, bird conservation has become a crucial part of the study. The paramount peril confronting avian species in jeopardy pertains to the degradation of their habitats. The chirping sound of birds can be heard with lots of noise and environmental sounds. So, monitoring and identification of bird sounds are more challenging. This study proposes meticulous monitoring and identifying bird sounds to ensure bird species' conservation and automate the remote monitoring of bird populations. Firstly, the data frame was generated from the acquired recorded dataset. Then, the data frames were segmented into slices of 0.5-second intervals so that the silent zones of the recorded dataset could be removed. Following this, unwanted noise was removed using the filter. Then, the Mel Frequency Cepstral Coefficients (MFCCs) spectrogram was generated using feature extraction methodology. From this spectrogram, a sound vector is generated. Monitoring and tuning different hyperparameter constraints using Principal Component Analysis (PCA), the model was subsequently trained using Dense Neural Network. The test demonstrates exceptional precision and reliability with a nominal margin of error.