Cost Effective Acoustic Monitoring of Bird Species

Climate change and human encroachment are some of the major threats facing several natural ecosystems around the world. To ensure the protection of ecosystems under threat, it is important to monitor the biodiversity within these ecosystems to determine when conservation efforts are necessary. For this to be achieved, technologies that allow large areas to be monitored in a cost effective manner are essential. In this work we investigate the use of acoustic recordings obtained using a low cost Raspberry Pi based recorder to monitor the Hartlaub’s Turaco in central Kenya. This species is endemic to East Africa and faces habitat loss due to climate change. Using simple features derived from the spectrograms of the recordings, a Gaussian mixture model classifier is able to accurately screen large data sets for presence of the Hartlaub’s Turaco call. In addition, we present a method based on musical note onset detection to determine the number of calls within a recording.