Time-Series Analysis for Ear-Related and Psychoacoustic Metrics Analysis f… · Psychoacoustics...

10-Jan-01 EURONOISE 2001 PATRAS

Time-Series Analysis for Ear-Related and Psychoacoustic Metrics

V. Mellert, H. Remmers, R. Weber, B. Schulte-Fortkamp


how to analyse p(t) to obtain an ear-related parameter?

• general remarks on acoustical analysis• ear-related signal processing• hearing sensation• sensation of vibration• time-frequency representations


acoustical analysis

• time related • level, envelope• statistical level-/ envelope-distributions• modulation-frequency analysis• estimates for impulsive sound


acoustical analysis

• related to spectral properties• weighting functions (A, B, C, ...)• octave, third-octave spectrum• high resolution Fourier – analysis• bandwidth × time-window ≅ 1


ear-related signal processing

• two ears• spatial hearing• source localisation• suppression of

disturbing noise• interaural correlation

Measuring system Head Acoustics


ear-related frequency analysis

excitation at low frequencies

p(t)

Basilar m

embrane

Cochlear

microm

echanics

head diffractionear canalImpedance -transformation

excitation at high frequencies


ear-related frequency analysis

p(t)

Basilar m

embrane

Cochlear

microm

echanics

head diffractionear canalImpedance -transformation

movie from Dept. of Neurophysiology, Univ. of Wisconsin – Madison http://www.neurophys.wisc.edu/h%26b/auditory/animation/animationidx.html


f(t)

Basilar m

embrane

resp. equivalent setof band-pass filters

low frequencies

high frequencies---------

spatial exitationtime dependant

excitationpattern

→ time→location

frequency → location


temporal integration

• nonlinear• loudness 100 – 200 ms• masking effects :

Cited from Zwicker/ Fastl:Psychoacoustics –Facts and Models


frequency (spatial) integration

• loudness: appr. 1/3 octave (bark-scale)• modulation synchrony (object forming, binding problem)

Cited from Zwicker/ Fastl:Psychoacoustics –Facts and Models


loudness summation scheme [Zwicker]

bandpass filters:1/3 ocatave, gam

ma-tone, ftt [Terhardt]

envelopelow passintegrationnon-linearity m

asking properties

sump(t) N(t)

envelopelow passintegrationnon-linearity


hearing sensations

• loudness [Zwicker]• sharpness, sensory

pleasantness [v. Bismark, Aures, Terhardt]


hearing sensations

• roughness [Aures,Terhard, Daniel]

• fluctuation strength [Fastl]




hearing sensations





algorithms for measurements partially available


hearing sensations





algorithms for measurements partially available

Comprehensive model of the functionality of the ear:„effective“ signal processing [Dau, Kollmeier]


sensation of vibration: thresholds

140 dB ≈ 1 g


threshold measurements: z


threshold measurements: y


vibration total value after ISO 2631-1/2

[ ] nanazW

nkz

nayW

nky

naxW

nkxV akakaka

1

,,, ++=



[ ] nanazW

nkz

nayW

nky

naxW

nkxV akakaka

1

,,, ++=

spatial weights

e.g. 0, ..., 1



[ ] nanazW

nkz

nayW

nky

naxW

nkxV akakaka

1

,,, ++=

frequency weighted vibration rms-values for x, y, z direction



[ ] nanazW

nkz

nayW

nky

naxW

nkxV akakaka

1

,,, ++=

21

0

2//, ))((1

= ∫

T

ySxyxW dttaT

a

21

0

2, ))((1

= ∫

T

SzzW dttaT

a


Just-noticable vibration difference value


multispectrum

• frequency resolution 1/3 octave, except for frequencies below 200 Hz

• time resolution, signal duration• loudness fluctuation: > 1 s• stationary: at least 200 ms• fluctuation strength: time window < 50 ms• roughness: time window < 5 ms


0 29 58 87 116 145 17416-80

100-160

200-250

315

400

500

630

800

1k

1.25k

1.6k

2k

2.5k

3.15k

4k

5k

time [ms]

freq

uenc

y [H

z]

0.0-2.5 2.5-5.0 5.0-7.5 7.5-10.0 10.0-12.5 12.5-15.0 15.0-17.5 17.5-20.0

interior noise of a passenger jetseat #4, 200 ms length, relative level in steps of 2.5 dB


0 29 58 87 116 145 17416-80

100-160

200-250

315

400

500

630

800

1k

1.25k

1.6k

2k

2.5k

3.15k

4k

5k

time [ms]

freq

uenc

y [H

z]

0.0-2.5 2.5-5.0 5.0-7.5 7.5-10.0 10.0-12.5 12.5-15.0 15.0-17.5 17.5-20.0

interior noise of a passenger jetseat #5, 200 ms length, relative level in steps of 2.5 dB


technical multispectrum

• linear transformations (log. amplitude)• seven 1/3 octaves below 80 Hz• three 1/3 octaves from 100 to 160 Hz• two 1/3 octaves from 200 to 250 Hz• additional „tracks“ of weighted vibration signal• time window 6 ms• signal duration 200 ms


towards an ear-related representation

• keeping time – frequency resolution• including masking effects (loudness meter)• object binding (identification of tonal

components)


part-tone time-pattern pttp[Terhardt, Heinbach, Mummert, 1985 – 1998]

time dependantexcitation

pattern

→ time→location

→ time

frequency←

contour-plot:line-pattern of significant features


• 4th order Fourier-time-transformation FTT [Terhardt] (gamma-tone, wavelet)

• identifiy local maxima (level 3 dB) in cuts parallel to frequency axis

• identify local changes in level with respect to time in cuts parallel to time axis

• connect identified points to lines according to threshold criteria

• Ommit short lines (texture) and keep „long“ ones (contour)


pttp-contour of a tyre noise


high-resolution pttp-contour of a helicopter in flight


multispectrum of a helicopter in flight

0 29 58 87 116 145 17416-80

100-160

200-250

315

400

500

630

800

1k

1.25k

1.6k

2k

2.5k

3.15k

4k

5k

time [ms]

freq

uenc

y [H

z]

0.0-2.5 2.5-5.0 5.0-7.5 7.5-10.0 10.0-12.5 12.5-15.0 15.0-17.5 17.5-20.0


technical realisation/ measurements

• no spatial information: mono microphone• acceleration in z sufficient (sensitivity)• single value for vibration in multispectrum• 200 ms time history (steady state)• time window for spectrum > 5 ms• no masking


summary and future developement• time-frequency representations are similar to the

excitation pattern of the ear• data reduction schemes like pttp can improve the

multispectrum representation• additional spectral analysis of the envelope

provides effective information about time fluctuations

• the images are an efficient input for pattern recognition schemes (like ANN)

Time-Series Analysis for Ear-Related and Psychoacoustic Metrics Analysis f… · Psychoacoustics...

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