An automatic classification of bird species using audio feature extraction and support vector machines

Automatic identification of bird species based on the chirping sounds of birds was experimented using feature extraction method and classification based on support vector machines (SVMs). The proposed technique followed the extraction of cepstral features on mel scale of each audio recording from the collected standard database. Extracted mel frequency cepstral coefficients (MFCCs) formed a feature matrix. This feature matrix was then trained and tested for efficient recognition of audio events from audio test signals. 70% of the whole database was used for training purpose while the remaining 30% for testing of samples. The classifier achieved upto 89.4% accuracy on a data set containing four species, commonly found in India.