1. Alaphangmagassag (virtual pitch) Terhardt (1972-74): megkulombozendo “virtual pitch” es “spectral pitch” dimenziok Virtual pitch: valoszinuleg (=biztos) idoelemzesbol adodik Spectral pitch: hangkepelemzes Ket eszleles: hangmagassag es hangszin

6. Alaphangmagassag erzekelese:idobe telik? (peldak) “Virtual pitch” hatara kb 1 kHz Dominans frekvenciaterulet (<1500 Hz) es felhangrendszamok (2-6) (Ritsma, 1962) Autokorrelacio reszlegesen megmagyarazza (Matlab peldak)

7. E Λ

8. Szurt zajnak autokorrelacioja alacsony, feher zajnak nulla

9. Feloldott es feloldatlan komponensek: Harmonikus felhangok linearisan (Hz) kovetik egymast, Mig a ful tonotopiai rendszere Greenwood egyenletet koveti

10. BM_mm = (16,7) log10 ((0,006046) freq + 1) (Greenwood egyenlőség) Basilar membrane (=alaphàrtya?)

11. Theorem proof obvious but no room to give it here...

12. Time-domain processing output of cochlea temporally structuredoutput of cochlea temporally structured neural circuitry specialized for timeneural circuitry specialized for time

13. Licklider

14. from cochlea

15. Licklider

16. Auditory Tuning Curve

17. period --> pitch

18. similar model, based on binaural interaction Licklider, Jeffress: excitatory interaction Jeffress

21. Harmonic cancellation: inhibitory interaction de Cheveigné

22. period --> pitch

23. medial superior olive (MSO)

24. Lateral superior olive (LSO)

25. Principle of MSO "coincidence counter" neuron Activated if impulses are simultaneous at input

26. Model by Jeffress (1948) Right ear Left ear Delay line

27. Principle of LSO "anti-coincidence counter" neuron activated except if impulses are simultaneous at input

28. Durlach model similar to Jeffress’s, based on binaural interaction (Equalization-Cancellation)

29. 2 types of model Correlation (auto- & cross-)Correlation (auto- & cross-) (excitatory interaction) CancellationCancellation (inhibitory interaction)

30. 2 types of model Correlation (auto- & cross-)Correlation (auto- & cross-) (excitatory interaction) CancellationCancellation (inhibitory interaction)

31. Basic ingredients running autocorrelation

32. Basic ingredients running autocorrelation

33. Basic ingredients running autocorrelation

34. Basic ingredients running autocorrelation

35. Basic ingredients running autocorrelation

36. Basic ingredients running autocorrelation

37. Basic ingredients running autocorrelation left

38. Basic ingredients running autocorrelation left right

39. Basic ingredients running autocorrelation left right running crosscorrelation

40. Structure 1 23 fast signal processing

41. 1. Licklider model of pitch E E

42. Module 1: calculate autocorrelation &Module 1: calculate autocorrelation & crosscorrelation for all t, ,  Module 2: select autocorrelation withModule 2: select autocorrelation with parameter  Module 3: vary  while monitoring outputModule 3: vary  while monitoring output of 2 for a maximum

43. 2. Jeffress model of localization Module 2: select crosscorrelation withModule 2: select crosscorrelation with parameter  Module 3: vary  while monitoring outputModule 3: vary  while monitoring output of 2 for a maximum

44. 4. Cancellation model of pitch

46. autocorrelation terms

47. Module 2: linear combination ofModule 2: linear combination of autocorrelation terms (parameter  Module 3: vary  while monitoring outputModule 3: vary  while monitoring output of 2 for a minimum 4. Cancellation model of pitch

48. 5. Equalization- Cancellation (Durlach)

50. autocorrelation & crosscorrelation terms

51. 6. Cancellation model of concurrent vowel identification delay tuned to period of interfering vowel templatematching

52. 6. Cancellation model of concurrent vowel identification

54. autocorrelation terms

55. Module 2: linear combination ofModule 2: linear combination of autocorrelation terms (parameters ,  Module 3: set T to cancel interfering vowel,Module 3: set T to cancel interfering vowel, vary , compare output of 2 to template 6. Cancellation model of concurrent vowel identification