1. A Study of Sparse Non-negative Matrix Factor 2-D Deconvolution Combined With Mask Application for Blind Source Separation of Frog Species 1 Reporter ： Jain-De Lee Advisor ： Wen-Ping Chen Department of Electrical Engineering National Kaohsiung University of Applied Sciences Network Application Laboratory

2. Outline Introduction and Motivation Introduction and Motivation Background Background Research Method Research Method Experiment Results Experiment Results Conclusion and Future Works Conclusion and Future Works Research Results Research Results 2

3. Introduction Current Technology of the Ecological Survey ◦ Sensor networks ◦ Wireless network Advantage ◦ Reduce the cost of human resource and time ◦ Save and share the raw data conveniently Disadvantage ◦ Large amount of raw data needs to be analyzed Voiceprint Recognition System ◦ Analyze raw data fast 3

4. Introduction 4

5. Introduction Blind Source Separation ◦ Cocktail party problem 5

6. Introduction Independent Subspace Analysis : ◦ M. A. Casey and A. Westner[2000]  Proceedings of the International Computer Music Conference ◦ Md. K. I. Molla and K. Hirose[2007]  IEEE Transactions on Audio, Speech, and Language Processing Wiener Filter : ◦ L. Bonaroya and F. Bimbot[2003]  International Symposium on Independent Component Analysis and Blind Signal Separation ◦ E. M. Grais and H. Erdogan[2011]  IEEE Digital Signal Processing, Sedona, Arizona Non-negative Matrix Factorization: ◦ P. Smaragdis[2004]  International Symposium on Independent Component Analysis and Blind Source Separation 6

7. Introduction Independent Component Analysis Combined with Other Methods : ◦ J. Lin and A. Zhang[2005]  NDT & E International ◦ M. E. Davies and C. J. James[2007]  Signal Processing ◦ X. Cheng, N. Li, Y. Cheng and Z. Chen[2007]  International Conference on Bioinformatics and Biomedical Engineering ◦ B. Mijović, M. D. Vos, I. Gligorijević, J. Taelman and S. V. Huffel[2010]  IEEE Transactions on Biomedical Engineering 7

8. Motivation Single Channel Blind Source Separation Preprocessing of Voiceprint Recognition System Improve Quality of Separated Signals 8

9. Background Post-processing Reconstruct Signal Blind Source Separation ICA 、 NMF Pre-processing Whitening 、 T-F Representation 9

10. Background Independent Component Analysis ◦ Looking for components of statistically independent from observational signals and estimating de-mixing matrix ◦ Constraint conditions  The components are statistically independent  At most one gaussian source is allowed  At least as many sensor responses as source signals ◦ Processing steps  Pre-processing  Centering  Whitening  Measurement of non-Gaussian component 10

11. Background Measurement of Non-Gaussian Component ◦ Kurtosis ◦ Mutual Information ◦ Neg-entropy Random Variable ykurt(y) Gaussiankurt(y) = 0 Non-Gaussian Super-Gaussiankurt(y) > 0 Sub-Gaussiankurt(y) < 0 11 J(Y): Neg-Entropy H(Y gauss ): Entropy of Gaussian Distribution H(Y): Entropy of Random Variable

12. 12 Background Non-negative Matrix Factorization

13. Background ◦ Cost function  Based on Euclidean Distance  Based on Kullback–Leibler Divergence 13 V : Original Signal : Reconstructed Signal

14. Background 14

15. Background Sparse Non-negative Matrix Factor 2-D Deconvolution (SNMF2D) ◦ Obtain temporal structure and the pitch change ◦ Control the sparse degree of non-negative matrix factorization Non-negative Matrix Factor 2-D Deconvolution ◦ τ basis matrix and φ coefficient matrix ◦ Shift operator Sparse Coding ◦ Take a few units to represent the data effectively ◦ Parts-based representations 15

16. Background 16 0

17. Background Sparse Non-negative Matrix Factor 2-D Deconvolution ◦ Cost function  Based on Euclidean Distance  Based on Kullback–Leibler Divergence λ:Sparse Factor f():Sparse Function 17

18. Background 18

19. Research Method 19

20. Research Method Pre-processing ◦ Time-domain signal converses to time-frequency signal  Analysis windows  Window function  Signal conversion 20

21. 研究方法 21

22. 研究方法 Reconstructed Signal of Latouche's Frog Reconstructed Signal of Sauter's Brown Frog 22

23. Mask Correction 23

24. Mask Correction Binary Mask ◦ The reconstructed signal converses to binary mask ◦ Find a suitable threshold T M(x,y): Binary Mask G(x,y): Reconstructed Signal 24

25. Mask Correction Otsu Method ◦ Create a histogram Element Number 25

26. Mask Correction TTTTTT L Element 26

27. Mask Correction 27

28. Mask Correction Signal Extraction V(x,y):Original Mixed-Signal S(x,y): Extraction of Signal 28

29. Mask Correction 29

30. Mask Correction Find a Ratio of Mixture Components ， G T (x,y): Sum of reconstructed signals G i (x,y): Reconstructed Signal R i (x,y): Ratio of mixture N: Total Numbers of reconstructed Signals 30

31. Mask Correction Signal correction ， : Revised Signals : Extraction of Signals 31

32. Mask Correction Signal correction ， : Corrected Signals 32

33. Mask Correction 33

34. 34

35. Post-processing Phase Information IDFT Window Function 35

36. Experiment Results Parameter ItemsParameter Value STFT Window Size512 samples Window Overlapping50% Window FunctionHamming Window Frequency Bin512 SNMF2D Basis Matrix[1…3] Coefficient Matrix[1…5] Sparse Factor5 Frog Species8 Mixtrue Items7 36

37. Experiment Results 37 Performance Measurement—SDR(Signal-to-Distortion Ratio)

38. 38

39. Experiment Results 39 Performance Measurement — SIR(Source-to-Interference Ratio)

40. 40

41. Experiment Results MethodIterationsVariance SNMF2D 3010.71275 507.56728 SNMF2D+MASK 3027.73557 5019.40138 41

42. Experiment Results Parameter ItemsParameter Value Frame Length512 samples Frame Overlapping50% Window FunctionHamming Window Frequency Bin512 Feature ParametersMel-Frequency Cepstral Coefficient Feature Dimensions15D Test Syllable410 42

43. Experiment Results Recognition Experiment MethodIterations Total Syllable Correct Syllable Accuracy(%) SNMF2D 3041020349.51 5041020048.78 8041020550 SNMF2D+MASK 3041031877.56 5041032378.78 8041033481.46 43

44. Conclusion and Future Works The proposed method ◦ Improve the quality of separated signals effectively ◦ Use less time to improve the quality of separated signals ◦ Enhance the recognition rate of separated signals, and the average recognition rate can be improved 29.84% 44

45. Conclusion and Future Works Future Works ◦ Study of de-noise methods ◦ Determine the numbers of species of raw data ◦ Study of the initial value setting ◦ Collect various sound of species. Then, Improve the recognition rate 45

46. Research Results 46 Competition ◦ 第七屆數位訊號處理創思設計競賽 — 入圍 Patent ◦ 蛙聲混音分離方法 — 審查中

47. 47 Thank you for your attention !!