Feature Vector Selection and Use With Hidden Markov Models to Identify Frequency-Modulated Bioacoustic Signals Amidst Noise

This paper describes an effective process for automated detection and classification of frequency-modulated sounds from birds, crickets, and frogs that have a narrow short-time frequency bandwidth. An algorithm is provided for extracting these signals from background noise using a frequency band threshold filter on spectrograms. Feature vectors are introduced and demonstrated to accurately model the resultant bioacoustic signals with hidden Markov models. Additionally, sequences of sounds are successfully modeled with composite hidden Markov models, allowing for a wider range of automated species recognition.