automatic detection and recognition of tonal bird sounds in noisy environments

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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.