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Dynamic time warping for automatic classification of killer whale vocalizations

Dynamic time warping for automatic classification of killer whale vocalizations. Judy Brown Patrick Miller. Physics Dept, Wellesley College Media-Lab MIT Sea Mammal Research Unit, University of St. Andrews. the Plan :.

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Dynamic time warping for automatic classification of killer whale vocalizations

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  1. Dynamic time warping for automatic classification of killer whale vocalizations Judy Brown Patrick Miller Physics Dept, Wellesley College Media-Lab MIT Sea Mammal Research Unit, University of St. Andrews

  2. the Plan : • Data: Pitch contours from two sets of vocalizations previously classified perceptually • Marineland group – 9 call types • Northern resident whales - 7 call types • Digression: two-source calls • Dynamic time warping for automatic classification of pitch contours • Results

  3. Pitch contours of Marineland set

  4. Pitch contours of Northern resident set

  5. Example with low freq and high freq sources (slowed by 6)

  6. Digression: Mechanism for Two SourcesCentral portion of Call 2.1ms -> 475 Hz (slowed by 6) .16 ms -> 6.2 kHz .113 s

  7. SIMULATION (slowed by 6) LOW FREQUENCY COMPONENT 450 - 900 Hz HIGH FREQUENCY COMPONENT 5800 - 6500 Hz

  8. SIMULATION LOW FREQUENCY COMPONENT 450 - 900 Hz (slowed by 6) HIGH FREQUENCY COMPONENT 5800 - 6500 Hz (slowed by 6)

  9. Dynamic Time Warping : the GoalCompare contours and quantify the difference.

  10. Difference Matrix D[i,j] = |T(j) - Q(i)|

  11. Difference Matrix and Cost Matrix D[i,j] = |T(j) - Q(i)| Example: insertion=1 deletion = 0 M =

  12. Cost Matrix with Query and Target Dissimilarity is M(qmax, tmax)

  13. Warping Function=Minimum Path

  14. Dissimilarity Matrix n1 n2 n32 n33 n4 n5 n9

  15. Clustering DISTANCES MULTIDIMENSIONAL SCALING (mds) Distances Positions in coordinate-like Space CLUSTERING – Form groups with minimum distanceswithin a group

  16. Clustering Results n32 n4 n5 n2 n33 n9 n1

  17. Another Look at n2, n4, n5, n9

  18. Results NORTHERN RESIDENT CALLS MARINELAND CALLS

  19. CONCLUSIONS • UPSIDE • HIGH ACCURACY for SEPARATED CONTOURS • VALIDITY of COMPARISON to HUMAN CLASSIFICATION ? • DOWNSIDE • TIME-CONSUMING MEASUREMENT of the PITCH CONTOURS

  20. Brown, J. C., A. Hodgins-Davis, and P. J. O. Miller (2006). “Classification of vocalizations of killer whales using dynamic time warping’’ J. Acoust. Soc. Am. 119, EL34-EL40. Acknowledgements: JCB thanks Dr. Eamonn Keogh for his MATLAB code which was adapted for some of the Figures. PJOM is grateful for funding from WHOI’s Ocean Life Institute and a Royal Society fellowship.

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