Ensemble extraction for classification and detection of bird species

Advances in technology have enabled new approaches for sensing the environment and collecting data about the world. Once collected, sensor readings can be assembled into data streams and transmitted over computer networks for storage and processing at observatories or to evoke an immediate response from an autonomic computer system. However, such automated collection of sensor data produces an immense quantity of data that is time consuming to organize, search and distill into meaningful information. In this paper, we explore the design and use of distributed pipelines for automated processing of sensor data streams. In particular, we focus on the detection and extraction of meaningful sequences, called ensembles, from acoustic data streamed from natural areas. Our goal is automated detection and classification of various species of birds. Key words: acoustics, data stream, ensemble, pattern recognition, remote sensing, time series analysis.

• Type: Journal
• Year: 2010
• Where: ECOI
• Authors: Eric P. Kasten, Philip K. McKinley, Stuart H. Gage
• Keywords: Data Streams, ECOI 2010, Sensor Data, Sensor Data Streams