Pitch of unresolved harmonics evidence against autocorrelation
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Pitch of unresolved harmonics: Evidence against autocorrelation. G rumble. Talk presented at “Pitch: Neural Coding and Perception” 4th-18th August, 2002, Hanse- Wissenschaftskolleg , Delmenhorst, Germany.

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Pitch of unresolved harmonics: Evidence against autocorrelation

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Pitch of unresolved harmonics evidence against autocorrelation

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

IntroductionPitch of unresolved harmonics

The ur-modelLicklider, 1951

The argumentKaernbach & Demany, 1998

ConfirmationKaernbach & Bering, 2001

Trying to convincePilot data

FailureShort survey on current models

Grumble


Interlude

Interlude

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


Pitch of unresolved harmonics

Spectrogram

Excitation pattern in the cochlea (LUTEar)

Pitch of unresolved harmonics

single note of a clavichord, 518 Hz


Processing of temporal structure

Processing of temporal structure

simplification:

slightly more complex:

 see Poster by Carsten Bogler


Studying temporal processing with clicks

simple periodic:

complex periodic:

aperiodic:

Studying temporal processing with clicks


Autocorrelation the ur model licklider 1951

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




1st versus 2nd order temporal regularity kaernbach and demany 1998

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


1st versus 2nd order temporal regularity kaernbach and demany 19981

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:kxxkxxxkxxxxabxx[0,10][0,10][0,10][0,10]ms

AC peak at55510ms

abx[0,5]

5


1st versus 2nd order temporal regularity kaernbach and demany 19982

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


Reducing the cut frequency kaernbach and bering 2001

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)


Simplifying

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].


Summary of evidence

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

=


Survey on current models

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


The pisa effect

The Pisa effect


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