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Automatic Detection and Recognition of Tonal Bird Sounds in Noisy Environments

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.

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Automatic Detection and Recognition of Tonal Bird Sounds in Noisy Environments

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  1. Automatic Detection and Recognition of Tonal Bird Sounds in Noisy Environments P. Jancovicand M. Kokuer EURASIP Journal on Advances in Signal Processing Volume 2011 • Presenter Chia-ChengChen

  2. Outline Introduction Detection of Bird Sounds Experimental Results Conclusions

  3. Introduction • Bird vocalisation is usually considered to be composed of calls and songs, which consist of a single syllable or a series of syllables. • Modellingof the bird sounds • Tonal-based feature • Gaussian mixture models

  4. Detection of Bird Sounds • A method for the detection of tonal regions of bird sounds • Spectral-level • Frame-level

  5. Detection of Bird Sounds（cont.） • Spectral-level • Hamming window • Sine-Distance • Postprocessing of the Sine-Distances

  6. Detection of Bird Sounds（cont.） • Sine-Distance • Postprocessing of the Sine-Distances • 2D median filter of size 15 × 3

  7. Detection of Bird Sounds（cont.） Figure 1: Waveform (a), spectrogram (b), and the corresponding sine-distance values

  8. Detection of Bird Sounds（cont.）

  9. Detection of Bird Sounds（cont.） • Frame-level • Comparing the results for the frame length • Frame length 32 、64 、128

  10. Detection of Bird Sounds（cont.）

  11. Detection of Bird Sounds（cont.）

  12. Detection of Bird Sounds（cont.） • Frame-level experimental results • Length 128 lowest performance • Length 64 at lower SNRs • Length 32 at higher SNRs

  13. Experimental Results

  14. Experimental Results（cont.）

  15. 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.

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