automatic detection and recognition of tonal bird sounds in noisy environments
DESCRIPTION
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 PresentationTRANSCRIPT
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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
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Mm w
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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.