Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models
- 1 April 2008
- journal article
- research article
- Published by Acoustical Society of America (ASA) in The Journal of the Acoustical Society of America
- Vol. 123 (4), 2424-2431
- https://doi.org/10.1121/1.2839017
Abstract
Behavioral and ecological studies would benefit from the ability to automatically identify species from acoustic recordings. The work presented in this article explores the ability of hidden Markovmodels to distinguish songs from five species of antbirds that share the same territory in a rainforest environment in Mexico. When only clean recordings were used, species recognition was nearly perfect, 99.5%. With noisy recordings, performance was lower but generally exceeding 90%. Besides the quality of the recordings, performance has been found to be heavily influenced by a multitude of factors, such as the size of the training set, the feature extraction method used, and number of states in the Markovmodel. In general, training with noisier data also improved recognition in test recordings, because of an increased ability to generalize. Considerations for improving performance, including beamforming with sensor arrays and design of preprocessing methods particularly suited for bird songs, are discussed. Combining sensor network technology with effective event detection and species identification algorithms will enable observation of species interactions at a spatial and temporal resolution that is simply impossible with current tools. Analysis of animal behavior through real-time tracking of individuals and recording of large amounts of data with embedded devices in remote locations is thus a realistic goal.This publication has 13 references indexed in Scilit:
- An Empirical Study of Collaborative Acoustic Source LocalizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Individual, age and sex-specific information is contained in yellow-bellied marmot alarm callsAnimal Behaviour, 2005
- Bird classification algorithms: theory and experimental resultsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Automated bioacoustic identification of speciesAnais da Academia Brasileira de Ciências, 2004
- Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative studyThe Journal of the Acoustical Society of America, 1998
- Birdsong recognition using backpropagation and multivariate statisticsIEEE Transactions on Signal Processing, 1997
- Template-based automatic recognition of birdsong syllables from continuous recordingsThe Journal of the Acoustical Society of America, 1996
- The Importance of Invariant and Distinctive Features in Species Recognition of Bird SongOrnithological Applications, 1989
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989
- Quantitative Analysis of Animal Vocal Phonology: an Application to Swamp Sparrow SongEthology, 1987