F. Briggs et al. (2013) The 9th annual MLSP competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment. Proceedings of 2013 IEEE International Workshop on Machine Learning for Signal Processing.
Author(s): Forrest Briggs; Yonghong Huang ; Raviv Raich ; Konstantinos Eftaxias ; Zhong Lei ; William Cukierski ; Sarah Frey Hadley ; Adam Hadley ; Matthew Betts ; Xiaoli Z. Fern ; Jed Irvine ; Lawrence Neal ; Anil Thomas ; Gábor Fodor ; Grigorios Tsoumakas ; Hong Wei Ng ; Thi Ngoc Tho Nguyen ; Heikki Huttunen ; Pekka Ruusuvuori ; Tapio Manninen ; Aleksandr Diment ; Tuomas Virtanen ; Julien Marzat ; Joseph Defretin ; Dave Callender ; Chris Hurlburt ; Ken Larrey ; Maxim Milakov
Abstract: Birds have been widely used as biological indicators for ecological research. They respond quickly to environmental changes and can be used to infer about other organisms (e.g., insects they feed on). Traditional methods for collecting data about birds involves costly human effort. A promising alternative is acoustic monitoring. There are many advantages to recording audio of birds compared to human surveys, including increased temporal and spatial resolution and extent, applicability in remote sites, reduced observer bias, and potentially lower cost. However, it is an open problem for signal processing and machine learning to reliably identify bird sounds in real-world audio data collected in an acoustic monitoring scenario. Some of the major challenges include multiple simultaneously vocalizing birds, other sources of non-bird sound (e.g., buzzing insects), and background noise like wind, rain, and motor vehicles.