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Acoustic classification of multiple simultaneous bird species

Acoustic classification of multiple simultaneous bird species F. Briggs et al, Journal of Acoustical Society of America, 2012. Motivation. How many of which bird is where? Habitat loss, climate change Birds are good indicators Current surveys Poor sampling Observer error/bias

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Acoustic classification of multiple simultaneous bird species

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  1. Acoustic classification of multiple simultaneous bird species F. Briggs et al, Journal of Acoustical Society of America, 2012

  2. Motivation • How many of which bird is where? • Habitat loss, climate change • Birds are good indicators • Current surveys • Poor sampling • Observer error/bias • Prohibitive costs

  3. Solution? • Automatic system! • Current systems • Single bird assumption • Controlled conditions • In practice… • Noisy recordings • Simultaneous bird songs

  4. In practice… • Brown creeper • Red-breasted nuthatch • Dark-eyed junco • Chestnut-backed chickadee

  5. Multiple Instance Learning! • Bag = Spectrogram of audio • Instance = Region of spectrogram • Features • Shape of mask • Statistics of time and frequency profiles • HOG features • 13 bird classes • Multi-class (multi-label) • One-against-all

  6. Experiments • 10232 instances in 548 bags • 50% instances labeled, others “noise”? • Single instance methods • Random forest (all instances + only labeled instances) • Predict bag label from instance labels • “Multi-class multi-instance” methods • …

  7. Conclusion • Cool dataset • Weird comparison (single vs multiple instance) • Multi-label aspect? • Label correlations

  8. Experiments • “MIML RBF” • Modified Hausdorff distances (meanmin) between bags • Until the best performance is found: • K-medoids clustering for each class (with different K) • Represent bags by RBF kernels to prototypes • Least-squares linear classifiers (one for each class)

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