1. Kaggle: Whale Challenge
張智星
多媒體資訊檢索實驗室
台灣大學 資訊工程系

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

3. Dataset Training set Test set Recording format
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 Noise 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 via Radon Transform
Projection onto lines at various angles
For grayscale images only
Detection objects at a specific angle

11. Example of Radon Transform
Source
Output
Code: goRadon.m

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

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

14. Visual Features via Hough Transform (2)
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
Image
Hough space and its maxima
Detected lines

16. Example of Hough Transform (2)
Source
(MATLAB code available)
Image
Edge image
Hough space and its maxima
Detected lines

17. Methods Thresholding Static classifiers Sequence classifiers
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 Advanced models Class 1: sil Class 2: sil-whale-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 (
ROC, DET
AUC