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Kaggle: Whale Challenge

Kaggle: Whale Challenge. 張智星 jang@cs.nthu.edu.tw http://www.cs.nthu.edu.tw/~jang 多媒體資訊檢索實驗室 台灣大學 資訊工程系. Whale Challenge. Problem definition Identify the existence of whales from sensor recordings Characteristics: Imbalance data Some recordings are hardly recognizable by non-experts.

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Kaggle: Whale Challenge

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  1. Kaggle: Whale Challenge 張智星 jang@cs.nthu.edu.tw http://www.cs.nthu.edu.tw/~jang 多媒體資訊檢索實驗室 台灣大學 資訊工程系

  2. Whale Challenge • Problem definition • Identify the existence of whales from sensor recordings • Characteristics: • Imbalance data • Some recordings are hardly recognizable by non-experts

  3. Dataset • Training set • 47,844 recordings of 2 seconds • 88.97% (42,565 recordings): w/o whales • 11.03% (5,276 recordings): with whales • Test set • 25,468 recordings of 2 seconds • Recording format • 2000-Hz sample rate, 16-bit resolution

  4. Preprocessing • Potential preprocessing • Trend removal • Trend estimation via polynomial fitting • Noise removal • Band-pass filter • Removal of “non-whale” part • Linear prediction?

  5. Spectrogram kwcPreprocess.m • W/o band-pass filter • W/ band-pass filter

  6. Potential Features • Acoustic features • Volume • Pitch • Spectrum • MFCC • … • Visual features (obtained from spectrogram) • Radon transform • Hough transform • Gabor filters • …

  7. Pitch Tracking • kwcPitchTracking.m

  8. Volume • kwcVolume.m

  9. Spectrogram • kwcSpectrogram.m

  10. Visual Features viaRadon Transform • Radon transform • Projection onto lines at various angles • For grayscale images only • Detection objects at a specific angle

  11. Example of Radon Transform • Source • http://www.mathworks.com/help/images/ref/radon.html • Output Code: goRadon.m

  12. Example of Radon Transform (2) • Source image • Output Code: goRadon2.m

  13. Visual Features viaHough Transform • Hough transform • Commonly used for detection lines and circles • For BW images only (after edge detection)

  14. Visual Features viaHough Transform (2) • Hough transform • Point to curve mapping • Two points  Two sine curves • The intersection is the right θ and ρ for the line connecting these two points

  15. Example of Hough Transform • Source • http://www.ebsd-image.org/documentation/reference/ops/hough/op/houghtransform.html Image Hough space and its maxima Detected lines

  16. Example of Hough Transform (2) • Source • http://www.mathworks.com/help/images/analyzing-images.html (MATLAB code available) Image Edge image Hough space and its maxima Detected lines

  17. Methods • Thresholding • Volume variance • Pitch variance • Static classifiers • Naïve Bayes classifiers • GMM • SVM • … • Sequence classifiers • HMM • CRF • …

  18. HMM Training • kwcHmmTrain.m

  19. HMM Evaluation • kwcHmmEval.m

  20. HMM • Basic models • Class 1: sil • Class 2: sil-whale-sil • Advanced models • sil • sil-whale-sil-whale-sil • … 1.0 sil 0.9 0.4 1.0 sil w sil 0.1 0.6

  21. HMM(2) • Other approach • Train HMM models • Align each recording with the HMM • Extract features from the whale part for other static classifiers • Duration (no. of frames) • Average log likelihood per frame 0.9 0.4 1.0 sil w sil 0.1 0.6

  22. Performance Evaluation • Performance evaluation of methods based on thresholding (http://en.wikipedia.org/wiki/Receiver_operating_characteristic): • ROC, DET • AUC

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