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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. - PowerPoint PPT Presentation
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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
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)
E Λ
Szurt zajnak autokorrelacioja alacsony, feher zajnak nulla
Feloldott es feloldatlan komponensek:
Harmonikus felhangok linearisan (Hz) kovetik egymast,
Mig a ful tonotopiai rendszere Greenwood egyenletet koveti
BM_mm = (16,7) log10 ((0,006046) freq + 1)(Greenwood egyenlőség)
Basilar membrane (=alaphàrtya?)
Theorem
(a b)2 a2 b2 2ab
proof obvious but no room to give it here...
Time-domain processing
•output of cochlea temporally structuredoutput of cochlea temporally structured•neural circuitry specialized for timeneural circuitry specialized for time
Licklider
Licklider
from cochleafrom cochlea
Licklider
Auditory Tuning Curve
02.5
57.5
0.1
0.34
0.77
1.5
2.8
0.5
kHz
lag (ms)
period --> pitchperiod --> pitch
similar model, based on similar model, based on binaural interactionbinaural interaction
Licklider, Jeffress: excitatory interactionLicklider, Jeffress: excitatory interaction
delayed s(t-T)
direct s(t)
fast synapses
Jeffress
Harmonic cancellation: inhibitory interactionHarmonic cancellation: inhibitory interaction
delayed s(t-T)
direct s(t)
I
E
de Cheveigné
02.5
57.5
0.1
0.34
0.77
1.5
2.8
0.5
kHz
lag (ms)
period --> pitchperiod --> pitch
medial superior olive (MSO)medial superior olive (MSO)
AVCN
cochlée
MSO
LSO
MNTB
DCN
PVCN
corps trapézoide
cochlée
AVCN
DCN
PVCN
bushy“bushy”
“bu
shy”
Lateral superior olive (LSO)Lateral superior olive (LSO)
AVCN
cochlée
MSO
LSO
MNTB
DCN
PVCN
corps trapézoide
cochlée
AVCN
DCN
PVCN
bushy“bushy”
“bu
shy”
Principle of MSOPrinciple of MSO
"coincidence counter" neuron"coincidence counter" neuron
Activated if impulses areActivated if impulses aresimultaneoussimultaneous at input at input
Model by Jeffress (1948)Model by Jeffress (1948)
Right earRight ear
Left earLeft ear Delay lineDelay line
Principle of LSOPrinciple of LSO
"anti-coincidence counter" neuron"anti-coincidence counter" neuron
activated activated except ifexcept if impulses impulsesare simultaneous at inputare simultaneous at input
MNTB
Durlach
model similar to Jeffress’s, based on model similar to Jeffress’s, based on binaural interactionbinaural interaction(Equalization-Cancellation)(Equalization-Cancellation)
2 types of model
•Correlation (auto- & cross-)Correlation (auto- & cross-)
(excitatory interaction)(excitatory interaction)•CancellationCancellation
(inhibitory interaction)(inhibitory interaction)
2 types of model
•Correlation (auto- & cross-)Correlation (auto- & cross-)
(excitatory interaction)(excitatory interaction)•CancellationCancellation
(inhibitory interaction)(inhibitory interaction)
Basic ingredients
xt
running autocorrelationrunning autocorrelation
Basic ingredients
xtL
running autocorrelationrunning autocorrelation
Basic ingredients
xtR
running autocorrelationrunning autocorrelation
Basic ingredients
xt xt
running autocorrelationrunning autocorrelation
Basic ingredients
x j x jjt1
tW
running autocorrelationrunning autocorrelation
Basic ingredients
rt () x jx jjt1
tW
running autocorrelationrunning autocorrelation
Basic ingredients
running autocorrelationrunning autocorrelation
rtL( ) x j
L x jL
jt1
tW
leftleft
Basic ingredients
running autocorrelationrunning autocorrelation
leftleft
rightright
rtL( ) x j
L x jL
jt1
tW
rtR( ) x j
Rx jR
jt1
tW
Basic ingredients
rtL( ) x j
L x jL
jt1
tW
running autocorrelationrunning autocorrelation
rtR( ) x j
Rx jR
jt1
tW
leftleft
rightright
running crosscorrelationrunning crosscorrelation
ct () x jLx j
R
jt1
tW
Structure
auto- and crosscorrelation
network
sound or filtered sound
LR
linear combination
control and
interpretationpitch, etc..
11 22 33fast signal processingfast signal processing
1. Licklider model of pitch
rt () x jx jjt1
tW
delayed x(t-)
direct x(t)
EE
EE
1. Licklider model of pitch
•Module 1: calculate autocorrelation & Module 1: calculate autocorrelation &
crosscorrelation for all t, crosscorrelation for all t, , , •Module 2: select autocorrelation withModule 2: select autocorrelation with
parameter parameter •Module 3: vary Module 3: vary while monitoring output while monitoring output
of 2 for a maximumof 2 for a maximum
2. Jeffress model of localization
•Module 2: select crosscorrelation withModule 2: select crosscorrelation with
parameter parameter •Module 3: vary Module 3: vary while monitoring output while monitoring output
of 2 for a maximumof 2 for a maximum
4. Cancellation model of pitch
dt ( ) (x j x jjt1
tW
)2
I
E
delayed x(t-)
direct x(t)
4. Cancellation model of pitch
dt ( ) (x j x jjt1
tW
)2
[x j2 x
j
2 2x j x jjt1
tW
]
4. Cancellation model of pitch
dt ( ) rt(0) rt (0) 2rt ( )
autocorrelation termsautocorrelation terms
•Module 2: linear combination of Module 2: linear combination of
autocorrelation terms (parameter autocorrelation terms (parameter •Module 3: vary Module 3: vary while monitoring output while monitoring output
of 2 for a minimumof 2 for a minimum
4. Cancellation model of pitch
5. Equalization-Cancellation (Durlach)
xtL x t
R
5. Equalization-Cancellation (Durlach)
dt ( ) (x jL x j
R )2
jt1
tW
5. Equalization-Cancellation (Durlach)
dt ( )rtL(0) rt
R (0) 2ct()
autocorrelation & autocorrelation & crosscorrelation termscrosscorrelation terms
6. Cancellation model of concurrent vowel
identification
delay tuned to perioddelay tuned to periodof interfering vowelof interfering vowel
delay
autocorrelation
templatetemplatematchingmatching
6. Cancellation model of concurrent vowel
identification
delay
zt xt xtT
rt
z () ztztjt1
tW
6. Cancellation model of concurrent vowel
identification
rt
z () (xt xtT)(x tjt1
tW
x tT )
6. Cancellation model of concurrent vowel
identification
rt
z () rt ( ) rtT ( T) rtT( ) rt ( T)
autocorrelation termsautocorrelation terms
•Module 2: linear combination ofModule 2: linear combination of
autocorrelation terms (parameters autocorrelation terms (parameters , , •Module 3: set T to cancel interfering vowel,Module 3: set T to cancel interfering vowel,
vary vary , compare output of 2 to template, compare output of 2 to template
6. Cancellation model of concurrent vowel
identification