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NOISE ROBUST BIRD SONG DETECTION USING SYLLABLE PATTERN-BASED HIDDEN MARKOV MODELS
 

Summary: NOISE ROBUST BIRD SONG DETECTION USING SYLLABLE PATTERN-BASED HIDDEN
MARKOV MODELS
Wei Chu
Department of Electrical Engineering
University of California, Los Angeles
Los Angeles, 90024
weichu@ee.ucla.edu
Daniel T. Blumstein
Department of Ecology and Evolutionary Biology
University of California, Los Angeles
Los Angeles, 90024
marmots@ucla.edu
1. ABSTRACT
In this paper, temporal, spectral, and structural characteristics of
Robin songs and syllables are studied. Syllables in Robin songs are
clustered by comparing a distance measure defined as the average
of aligned LPC-based frame level differences. The syllable patterns
inferred from the clustering results are used for improving the acous-
tic modelling of a hidden Markov model (HMM)-based Robin song
detector. Experiments conducted on a noisy Rocky Mountain Bi-

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences