pitch of unresolved harmonics: evidence against autocorrelation
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Pitch of unresolved harmonics: Evidence against autocorrelationPitch of unresolved harmonics: Evidence against autocorrelation
Christian Kaernbach and Carsten BoglerInstitut für Allgemeine Psychologie, Universität Leipzig
Talk presented at
“Pitch: Neural Coding and Perception”4th-18th August, 2002, Hanse-Wissenschaftskolleg, Delmenhorst, Germany
• 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
Grumble
InterludeInterlude
Fugue G-major by Johann Matthesonfrom “Wohlklingende Fingersprache”performed by Gisela Gumz, Clavichord
single note of a clavichord, 518 Hz
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Pitch of unresolved harmonicsPitch of unresolved harmonics
Spectrogram
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Excitation pattern in the cochlea (LUT Ear)
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simplification:
slightly more complex:
Processing of temporal structureProcessing of temporal structure
see Poster by Carsten Bogler
Frequency
En
erg
y
Studying temporal processing with clicksStudying temporal processing with clicks
simple periodic:
complex periodic:
aperiodic:
Time
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Frequency
Ene
rgy
Autocorrelation: The ur-modelLicklider, 1951
Autocorrelation: The ur-modelLicklider, 1951
from cochlea
delay line
fast line
coincidencecells
Autocorrelation in generalAutocorrelation in generals(t) s(t-)
w(t-t0)
dt(s(t)) s(t-): triggered correlation (AIM)
s(t) = the stimuluscochlea excitationsimulated spike trains + coincidencerecorded spike trains + coincidence
AC(,t0) =
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1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998
1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998
kxx: k = 5ms, x [0,10] ms
k k k
abx: a [0,10] ms, b = 10 - a, x [0,10] ms
a b a b a b
kxxx kxxxx
high-pass filtered, low-pass masked, Fc = 6 kHz
x
kxx
1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998
1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998
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num
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chance performanceJLRSCSLLD
target type: kxx kxxx kxxxx abxx [0,10] [0,10] [0,10] [0,10] ms
AC peak at 5 5 5 10 ms
task: discriminate regular sequence from random sequenceprocedure: adaptive reduction of the length of the sequence
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1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998
1st- versus 2nd-order temporal regularityKaernbach and Demany, 1998
kxx: k = 5ms, x [0,10] ms
k k k
abx: a [0,10] ms, b = 10 - a, x [0,10] ms
a b a b a b
kxxx kxxxx
high-pass filtered, low-pass masked, Fc = 6 kHz
x
kxx
=
Reducing the cut frequencyKaernbach and Bering, 2001
Reducing the cut frequencyKaernbach and Bering, 2001
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JND
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Fo = 100 HzFo = 150 HzFo = 250 HzHoutsma & Smurzynski
pitch JNDs for periodic click sequences,high-pass filtered, low-pass masked,
for 15 subjects
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kxx abx
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ect
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IFC
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completesemidichotic
confirm Kaernbach & Demanywith cut frequency = 2 kHz
(x [0,15] ms)
SimplifyingSimplifying• 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].
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K&B 2001
2nd order
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Summary of evidenceSummary of evidence
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Further evidence:• Carlyon, 1996
mixture of two complex tonescomposed of unresolved harmonicswith different F0 produces no clear-cut pitch percept
• Plack & White, 2000pitch shifts due to variations of a gapbetween two click sequencesare incompatible with autocorrelation
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Survey on current modelsSurvey on current models
JASA online searchautocorrelation <not> (abstract <in> type)
psychological acousticsrevised after 9/1998
applying/advocating autocorrelation
AppealAC modelers: test your models
with 2nd-order regularities
publish results (positive or negative)
eventually: modify your models
AppealAC modelers: test your models
with 2nd-order regularities
publish results (positive or negative)
eventually: modify your models
The Pisa effectThe Pisa effect