Psychoacoustics of Dynamic ‘Center-of-Gravity’ Signals

Larry FethAshok KrishnamurthyOhio State University

Spectral Center-of-Gravity Chistovitch and Lublinskaja

(1976,1979)

Perceptual Formant at ‘Center-of-Gravity’

Two-formant synthetic vowel Matched by adjustable single-formant signal Center frequency of match depends on

relative amplitudes of the two formants

Experimental Paradigm

Chistovitch and Lublinskaja Results

Voelcker Two-tone Signals

Voelcker Two-tone Signals

Initially, led to the EWAIF model Envelope-Weighted Average of

Instantaneous Frequency (time domain) Point by point multiply E x F values Sum over N periods Divide by sum of weights

Indicates pitch change in periodic signals

Helmholtz (1954, 2nd English edition) Jeffress (1964)

EWAIF Model

Signal ( ) with envelope ( )

instantaneous frequency ( ).

EWAIF (Envelope weighted

average of instantaneous frequency)

( ) ( )EWAIF[ ( )]

( )

x t e t

i t

e t i t dtx t

e t dt

IWAIF Model Predictions

Two-tone resolution task Feth and O’Malley (1977)

Two-tone resolution I = 1 dB; f independent variable ‘Voelcker-tone pair’ pitch discrimination inverted “u-shaped” psychometric

functions Components resolved beyond –75% point ~3.5 Bark separation = jnnd

Voelcker Signal: Discrimination Task

Discrimination Results

Jnnd – ‘Just not noticeable difference’

Filled circles Breakpoint estimates

Open circles CR – critical ratio CBW CB – ‘empirical’ CBW Solid line TW envelope

IWAIF Model

Intensity Weighted Average of Instantaneous Frequency = Centroid of signal’s positive power spectrum (Anantharaman, et al., 1993)

2

2

20

20

( ) ( )IWAIF[ ( )]( )

| ( )|

| ( )|

e t i t dtx te t dt

f X f df

X f df

Dynamic Center-of-Gravity Effect

Lublinskaja (1996) Three-formant synthetic Russian vowels Listeners identified vowels with:

‘conventional’ formant transitions co-modulated formant pairs that exhibit the same

dynamic spectral center-of-gravity ID functions were very similar with formant

pairs separated by 4.3 Bark or less

Psychophysics

Anantharaman (1998) Two-tone signals with dynamic c-o-g

effect We called them ‘Virtual Frequency’

Glides Listeners matched transition rates in

VF glides to those in FM glides IWAIF model predicts results for

transitions from 2 to ~5 ERB

Dynamic Center-of-Gravity Signals

Waveform

Long-term Spectrum

Spectrogram

Rate-matching results

Model Results

Short-term running IWAIF Model

IWAIF Model Results

Application of ST-IWAIF Model

More Psychophysics

Research Question(s) What is being ‘integrated’ in spectral

integration?OR

Where in the auditory system is the processing located?

Psychophysics Iyer, et al., (2001)

Temporal acuity for FM and VF glides Step vs. linear ramp discrimination Similar T values may mean common

process

Masking patterns for FM and VF glides Peripheral process i.e., ‘Energy Masking’ Different results – VF not peripheral process

Temporal Acuity Paradigm

Step (red) versus Glide (blue) transitions for FM tone (left panel) and Virtual Frequency (right panel)

Temporal Acuity Results

Just discriminable step duration for FM (solid lines; filled symbols) and VF (dashed lines; unfilled symbols) signals. Frequency separations are 2, 5 and 8 ERBu. The results for 1000 Hz are represented by circles and those for 4000 Hz by triangles. Average for 4 listeners.

Frequency separation (ERBu)

2 ERBu 5 ERBu 8 ERBu

Step

Dur

ation

(mse

c)

0

2

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18

20

Dynamic Center-of-Gravity Maskers

Masking of brief probe by FM glide (left panel) and by VF glide (right panel). Probe is in the spectro-temporal center of each masker. Five auditory filter bands are illustrated.

Time

Fl

Fc

Fh

Time

Fl

Fc

Fh

Masking Results

Masking of a 20 ms probe by FM (light blue) and VF (darker blue) maskers. The probe is placed at the beginning, middle, and end of the masker. Significant differences are seen at 5 and 8 ERB for the middle position and the initial position at 8 ERB. Average for 4 listeners.

Probe in initial position

0.00

5.00

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25.00

30.00

35.00

2 ERBu 5 ERBu 8 ERBu

Frequency separation

Am

ou

nt

of

Ma

sk

ing

(d

B S

PL

)

FM Masker

VF Masker

Probe in medial position

0.00

5.00

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30.00

35.00

2 ERBu 5 ERBu 8 ERBu

Frequency separation

Am

ount

of M

aski

ng (d

B SP

L)

FM Masker

VF Masker

Probe in final position

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5.00

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30.00

35.00

2 ERBu 5 ERBu 8 ERBu

Frequency separation

Am

ount

of M

aski

ng (d

B S

PL)

FM Masker

VF Masker

Glide Direction Asymmetry Gordon and Poeppel

3 Frequency ranges: (for F1,F2 & F3) ~ 30 unpracticed listeners 20 trials / signal One interval Direction Identification: Up vs. Dn

Best results at high frequency (F3) range 10- through 160 ms ‘Up’ is easier to ID than ‘Dn’ Less clear-cut results at low or mid-freq. ranges

Glide Direction Asymmetry

Gordon and Poeppel – ARLO (2002)Identification of FM Sweep direction is easier for rising than for

falling tones.

Glide Direction Asymmetry Dawson, (2002)

Tested only high frequency range (F3) Practiced listeners; ~ 100% all

conditions! Modified procedure

Rove each frequency sweep over 1 octave

Practice to ~ asymptote

Glide ID Results Average for 4

listeners One-interval ID task 250 trials / datum

point Well-practiced Subj’s Starting frequency

roved over 1-octave range

Summary FM ‘easier’ than VF Up ‘easier’ than Down

Duration (ms)

5 10 20 30 40 50 80 160

Perc

ent C

orre

ct Id

entif

icat

ion

50

55

60

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90

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100

FM Up

FM Down

VF Up

VF Down

CV Identification Experiment

[da] – [ga] continuum: varying F3 transition Duration: 50 ms transition into 200 ms base F3 onset: 2018 to 2658 Hz in 80 Hz steps F3 base: 2527 Hz (constant)

Formant transition ‘type’: Klatt synthesizer Frequency Modulated tone glide Virtual Frequency glide

CV Identification: Stimuli

Spectrogram 1. Step 1 of Klatt Monaural Continuum—/ga/ endpoint

CV Identification: Stimuli

Spectrogram 2. Step 1 of FM Monaural Continuum—/ga/ endpoint

CV Identification: Stimuli

Spectrogram 3. Step 1 of VF Monaural Continuum—/ga/ endpoint

CV Identification: Stimuli

Spectrogram 4. Step 1 of Dichotic FM Continuum—/ga/ endpoint

CV Identification: Stimuli

Spectrogram 5. Step 1 of Dichotic VF Continuum—/ga/ endpoint

CV Identification Experiment

Listeners: 8 adults with normal hearing

Procedure: One interval, 2-AFC 3 transition types: Klatt, FM or VF 6 of 8 tokens tested 20 repetitions / token

Results are averaged for the 8 listeners

CV Identification: Results

Fig. 4. Mean Responses for FM Tone and Virtual Glide Conditions

Formant 3 Onset Frequency (in Hz)

1900 2000 2100 2200 2300 2400 2500 2600 2700

% /

da

/ R

esp

on

ses

0

20

40

60

80

100FM

Virtual Glide

CV Identification: ResultsFig. 7. Mean Responses for Dichotic Condition

F3 Onset Frequency (Hz)

1900 2000 2100 2200 2300 2400 2500 2600 2700

% /d

a/ R

espo

nses

10

20

30

40

50

60

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80

90

Klatt

FM

Virtual Glide

Psychoacoustics of Dynamic ‘Center-of-Gravity’ Signals

Conclusions ‘Excitation’ is integrated not signal

energy The processing is central not peripheral

Masking Patterns are very different Temporal Acuity results are similar for FM & VF

glides Direction ID Asymmetry is similar for FM & VF

glides

Psychoacoustics of Dynamic ‘Center-of-Gravity’ Signals

Conclusions CV identification functions are similar for:

Klatt synthesized sounds FM formant sounds VF formant sounds

Thus, it doesn’t matter how ‘excitation’ is moved from A to B, the brain will interpret it as the same sound.

The effect is evident under dichotic listening; further support for central processing.

Collaborators

Rob FoxNandini Iyer

Jayanth Anantharaman

Ewa Jacewicz Robin Dawson

Psychoacoustics of Dynamic ‘Center-of-Gravity’ Signals

Thank You

Questions?

Up vs. Down FM Glide

Up vs. Down FM Glide

Up vs. Down VF Glide

Up vs. Down VF Glide

Effect of Masker Direction

Masking produced by VF (above) and FM (below) maskers with F = 5 ERB. Purple bars are “up” glides; yellow bars are “down” glides. Centered probe.

Effect of Masker Position

Masking produced by VF (above) and FM (below) maskers with F = 5 ERB. Purple bars are “up” glides; yellow bars are “down” glides.

Klatt & FM Parameters

Fig. 1. Formant 3 Transitions for Klatt and FM Tokens

Time (ms)

0 10 20 30 40 50 60

Fre

qu

ency

1900

2000

2100

2200

2300

2400

2500

2600

2700

Step 1 (/ga/ endpoint)Step 2 Step 3 Step 4 Step 7 Step 8 (/da/ endpoint)

Virtual Frequency Parameters

Fig. 2. Tone Amplitude Changes in Step 1 (/ga/) of VG Continuum (2018 Hz virtual onset)

Time (ms)

0 10 20 30 40 50 60

Rel

ativ

e A

mpl

itud

e

0.0

0.2

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0.8

1.0 Tone 1 (2018 Hz)Tone 2 (2658 Hz)

Fig. 3. Tone Amplitude Changes in Step 8 (/da/) ofVG Continuum (2578 Hz virtual onset)

Time (ms)

0 10 20 30 40 50 60

Rel

ativ

e A

mpl

itud

e

0.0

0.2

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0.6

0.8

1.0

Tone 1 (2018 Hz) Tone 2 (2658 Hz)