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Automatic Detection and Recognition of Tonal Bird Sounds in Noisy Environments. P. Jancovic and M. Kokuer EURASIP Journal on Advances in Signal Processing Volume 2011. Presenter Chia -Cheng Chen. Outline. Introduction Detection of Bird Sounds Experimental Results Conclusions. - PowerPoint PPT Presentation
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Automatic Detection and Recognition of Tonal Bird Sounds in Noisy Environments
P. Jancovic and M. Kokuer
EURASIP Journal on Advances in Signal ProcessingVolume 2011
Presenter Chia-Cheng Chen
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Outline
Introduction Detection of Bird Sounds Experimental Results Conclusions
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Introduction
Bird vocalisation is usually considered to be composed of calls and songs, which consist of a single syllable or a series of syllables.
Modelling of the bird sounds Tonal-based feature Gaussian mixture models
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Detection of Bird Sounds
A method for the detection of tonal regions of bird sounds Spectral-level Frame-level
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Detection of Bird Sounds（ cont.）
Spectral-level Hamming window Sine-Distance Postprocessing of the Sine-Distances
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Detection of Bird Sounds（ cont.）
Sine-Distance
Postprocessing of the Sine-Distances 2D median filter of size 15 × 3
2
)0(
)(
)(12
1)(s
M
Mm w
mw
kS
mkS
Mkd
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Detection of Bird Sounds（ cont.）
Figure 1: Waveform (a), spectrogram (b), and the corresponding sine-distance values
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Detection of Bird Sounds（ cont.）
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Detection of Bird Sounds（ cont.）
Frame-level Comparing the results for the frame
length Frame length 32 、 64 、 128
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Detection of Bird Sounds（ cont.）
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Detection of Bird Sounds（ cont.）
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Detection of Bird Sounds（ cont.）
Frame-level experimental results Length 128 lowest performance Length 64 at lower SNRs Length 32 at higher SNRs
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Experimental Results
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Experimental Results（ cont.）
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Conclusions
MFCC features provide extremely low recognition performance even in mild noisy conditions at the SNR of 10 dB.
Employing a multiple-hypothesis recognition approach.
