Bird Species Identification using Audio Processing and AlexNet Neural Network (International Journal of Food and Nutritional Sciences, 2022)
This research involved identifying the birds with the help of audio recorded from the realworld environment. Most of the methods use images for detection of birds. But, somespecies may be similar to see. Hence, we took audio as the basis for classification. Theaudio frequency was plotted as spectrogram and it was inspected to extract the patternsand classify the bird. Legacy practices involved manual inspection of spectrogram that areplotted on the frequency of audio signals. But, this is time taking process and oftenproduces inaccurate results. Hence, we created a computerized process to inspect thespectrogram. The computer learns from patterns of spectrogram during the training processand learns to detect a new and unseen audio. This entire procedure involved two crucialphases. The first stage of the process was to create a dataset with audio files collected fromwebsites like Xeno-canto org that includes all sound recordings of birds. In this researchwork, we considered 4 species of wood pecker in the Germany region. Hence, we havecollected approximately 120 recordings for each species, thus a total of 500 recordings werecollected. The collected sounds undergone a series of pre-processing phases likereconstruction, framing, and silence removal, pre-emphasis for removing any noises likehuman actions, wind sounds, tree sounds. For every processed sound clip, the spectrogramis plotted and it was given as input to a neural network that is in the second stage, which inturn detects the recording at the end. Since the image was given as input, we usedConvolutional Neural Network (CNN) which is a best neural net in deep learning for text andimage based tasks. The CNN categorizes sound clip and determines the species of birdbased on input features. A model was created and put into practice.