Automatic identification of bird calls using Spectral Ensemble Average Voice Prints

Automatic identification of bird calls without manual intervention has been a challenging task for meaningful research on the taxonomy and monitoring of bird migrations in ornithology. In this paper we apply several techniques used in speech recognition to the automatic identification of bird calls. A new technique which computes the ensemble average on the FFT spectrum is proposed for identification of bird calls. This ensemble average is computed on the FFT spectrum of each bird and is called the Spectral Ensemble Average Voice Print (SEAV) of that particular bird. The SEAV of various birds are computed and are found to be different when compared to each other. A database of bird calls is created from the available recordings of fifteen bird species. The SEAV is then used for the identification of bird calls from this database. The results of identification using SEAV are then compared against the results derived from common classifiers used in speech recognition like dynamic time warping (DTW), Gaussian mixture modeling (GMM). A one level and two level classifier combination is also tried by combining SEAV classifier with the DTW classifier. The SEAV is computationally less expensive when compared to DTW or the GMM based classifiers while performing better than the DTW technique. Several new possibilities in automatic bird call identification using SEAV are also listed.