  Audio sound recognition and classification is used for many tasks and
applications including human voice recognition, music recognition and audio
tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in
combination with a range of machine learning models to identify (Australian)
birds from publicly available audio files of their birdsong. We present
approaches used for data processing and augmentation and compare the results of
various state of the art machine learning models. We achieve an overall
accuracy of 91% for the top-5 birds from the 30 selected as the case study.
Applying the models to more challenging and diverse audio files comprising 152
bird species, we achieve an accuracy of 58%
