EE BIOL 135 - Population Genetics
EE BIOL 19 - Fiat Lux Freshman Seminars
EE BIOL 235 - Population Genetics
Our lab would like to understand the grammar and meaning of bird songs. Bird songs are not random; they do have rules for putting notes and phrases together. We would like to characterize those rules. Recent advances in sensor arrays, computation, and computational linguistics finally make this long-sought goal achievable. The approach taken by our laboratory is to: (1) collect a large amount of bird song recordings from hand-held or acoustic sensor arrays in a variety of natural settings; (2) process the data by software, some of which is recent and some of which we are developing with new advances in localizing sources with beamforming, then filtering out noise, identifying events of interest, and then classifying them according to species and individual, combining that with behavioral observations when possible; (3) this information/knowledge is then stored in a large database that is shared among collaborating research groups; and (4) analyzing the recordings by computational-linguistic tools to identify the syntax of the songs. Combined that linguistic understanding with information about the context in which it occurred, our aim to explore new software methods to identify the meaning of those songs. We collaborate with ecologists, engineers, linguists and artists.
Our work is contributing to a profound transformation that is already underway: the recognition of very sophisticated signaling strategies and syntactic structures in non-human species. The new tools and methods for collecting and analyzing bird song now allow a level of observation that previously would not have been possible. We are now collecting truly vast amounts of data from previously inaccessible settings and subjecting data to previously undiscovered sophisticated structural analyses. Our work may also be transformational to computational linguistics if the natural world beyond humans were shown to have languages that are radically different from our own (as seems quite likely).
Zhang, J, G. Kossan, R. Hedley, R. Hudson, C. E. Taylor, K. Yao and M. Bao, "Fast 3D AML-based Bird Song Estimation", Unmanned Systems, 2 : 249- (2014) .
Arriaga, J. G., Sanchez, H., Hedley, R., Vallejo, E. E., and Taylor, C. E., "Using Song to Identify Cassin?s Vireo Individuals. A Comparative Study of Pattern Recognition Algorithms", Pattern Recognition, Springer International Publishing 2 : 291-300 (2014) .
Tan, Lee Ngee, George Kossan, Martin L. Cody, Charles E. Taylor, Abeer Alwan, "A sparse representation-based classifier for in-set bird phrase verification and classification with limited training data", The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 16-31, 2013, 2 : - (2013) .
Sasahara K, Cody ML, Cohen D, Taylor CE., "Structural Design Principles of Complex Bird Songs: A Network-Based Approach", PLoS ONE, 7 (9): e44436- (2012) .
Kirschel, A.N.G., M.L. Cody, Z. Harlow, V.J. Promponas, E.E. Vallejo and C.E. Taylor, "Territorial dynamics of Mexican Antthrushes revealed by individual recognition of their songs", Ibis, 153 : 255-268 (2011) .