Pitch of unresolved harmonics: Evidence against autocorrelation

1. Pitch of unresolved harmonics: Evidence against autocorrelation Grumble Talk presented at“Pitch: Neural Coding and Perception” 4th-18th August, 2002, Hanse-Wissenschaftskolleg, Delmenhorst, Germany Christian Kaernbach and Carsten BoglerInstitut für Allgemeine Psychologie, Universität Leipzig Introduction Pitch of unresolved harmonics The ur-model Licklider, 1951 The argument Kaernbach & Demany, 1998 Confirmation Kaernbach & Bering, 2001 Trying to convince Pilot data Failure Short survey on current models Grumble

2. Interlude Fugue G-major by Johann Matthesonfrom “Wohlklingende Fingersprache”performed by Gisela Gumz, Clavichord

3. Spectrogram Excitation pattern in the cochlea (LUTEar) Pitch of unresolved harmonics single note of a clavichord, 518 Hz

4. Processing of temporal structure simplification: slightly more complex:  see Poster by Carsten Bogler

5. simple periodic: complex periodic: aperiodic: Studying temporal processing with clicks

6. Autocorrelation: The ur-modelLicklider, 1951 fast line coincidencecells from cochlea delay line Autocorrelation in general  AC(,t0) = s(t)  s(t-)  w(t-t0) dt (s(t)) s(t-): triggered correlation (AIM) s(t) = the stimulus cochlea excitation simulated spike trains + coincidence recorded spike trains + coincidence 

7. k k k a b a b a b 1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998 kxx: k = 5ms, x [0,10] ms kxx high-pass filtered, low-pass masked, Fc = 6 kHz kxxx kxxxx x abx: a [0,10] ms, b = 10 - a, x [0,10] ms abx

8. 1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998 task: discriminate regular sequence from random sequence procedure: adaptive reduction of the length of the sequence target type: kxx kxxx kxxxx abx x [0,10] [0,10] [0,10] [0,10] ms AC peak at 5 5 5 10 ms abx [0,5] 5

9. 1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998 kxx: k = 5ms, x [0,10] ms kxx k k k high-pass filtered, low-pass masked, Fc = 6 kHz  kxxx kxxxx x  = abx: a [0,10] ms, b = 10 - a, x [0,10] ms abx ab ab ab

10. Reducing the cut frequencyKaernbach and Bering, 2001 pitch JNDs for periodic click sequences,high-pass filtered, low-pass masked,for 15 subjects confirm Kaernbach & Demanywith cut frequency = 2 kHz(x  [0,15] ms)

11. Simplifying • abx & kxx too complicated. • ab = periodic sequence + interfering clicks • Kaernbach & Demany 1998: vary amplitude of interfering clicks • vary cut frequency, compare with jnd (cf. Kaernbach & Bering, 2001)ab with a [0,4], b = 8 - a, versus xy with x [0,4], y [4,8].

12. kxx kxxx x xy kxxxx ab abx Summary of evidence  • Further evidence: • Carlyon, 1996mixture of two complex tonescomposed of unresolved harmonicswith different F0produces no clear-cut pitch percept • Plack & White, 2000pitch shifts due to variations of a gapbetween two click sequencesare incompatible with autocorrelation =

13. Appeal AC modelers: test your models with 2nd-order regularities publish results (positive or negative) eventually: modify your models Survey on current models JASA online search autocorrelation <not> (abstract <in> type) psychological acoustics revised after 9/1998 applying/advocating autocorrelation

14. The Pisa effect