Birds Sound Classification Based on Machine Learning Algorithms
Aska E. Mehyadin, Adnan Mohsin Abdulazeez, Dathar Abas Hasan, Jwan N. Saeed
Asian Journal of Research in Computer Science · pp. 1–11 · Published 16 Jun 2021
10.9734/ajrcos/2021/v9i430227Abstract
The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).
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