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Acoustics Research Institute. Austrian Academy of Sciences. Additivity of auditory masking using Gaussian-shaped tones a Laback, B., a Balazs, P., a Toupin, G., b Necciari, T., b Savel, S., b Meunier, S., b Ystad, S., and b Kronland-Martinet, R.

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Acoustics Research Institute

Austrian Academy of Sciences

  • Additivity of auditory masking

  • using Gaussian-shaped tones

  • aLaback, B., aBalazs, P., aToupin, G., bNecciari, T., bSavel, S., bMeunier, S., bYstad, S., and bKronland-Martinet, R.

  • aAcoustics Research Institute, Austrian Acad. of Sciences, Austria

  • bLaboratoire de Mécanique et d'Acoustique, CNRS Marseille, France

  • MULAC Meeting, Vienna

  • Sept 24rd, 2008

  • [email protected]

  • http://www.kfs.oeaw.ac.at


Motivation
Motivation

  • Both temporal and frequency masking have been studied extensively in the literature

  • Very little is known about their interaction, i.e., masking in the time-frequency domain

  • An accompanying study (Necciari et al., this conference) presents data on time-frequency time-frequency masking caused by a Gaussian-shaped tone pulse (“Gaussian”)

  • Our aim is to study the additivity of masking from multiple Gaussian maskers

  • Taken together, these data may serve as a basis to model time-frequency masking in complex signals


Tim frequency masking
Tim-frequency masking

frequency

time


Tim frequency masking1
Tim-frequency masking

frequency

time


Tim frequency masking2
Tim-frequency masking

frequency

time


Outline
Outline

  • 3 steps:

    • Additivity of temporal masking

    • Additivity of frequency masking

    • Additivity of time-frequency masking (not presented today)


Experiment design
Experiment design

  • Both signal and maskers are Gaussian-windowed tones:

    with Γ: gamma factor: (Γ = α.f0), where f0 is the tone frequency and α the shape factor

    • Equivalent rectangular bandwidth ( Γ): 600 Hz

    • Equivalent rectangular duration: 1.7 ms

  • Good properties of Gaussian in time-frequency domain:

    • Minimal spread in time-frequency

    • Gaussian shape in both time and frequency

  • A study by van Schijndel et al. (1999) has shown that Gaussian-windowed tones with an appropriate alpha factor may fit the auditory time-frequency window.


Experiment design1
Experiment design

  • Procedure:

    • 3 interval - 3 AFC (oddity task)

    • Adaptive procedure: 3 down - 1 up rule (estimates the 79.4% threshold)

    • 12 turnarounds, the last 8 used to calculate the threshold

    • Stepsize: 5 dB, halved after 2 turnarounds

  • Repeated measurements to have at least three stable values

  • Presented in blocks of equivalent number of maskers

  • Five subjects, normal hearing according to standard audiometric tests


Additivity of temporal masking design
Additivity of temporal maskingDesign

  • Frequency (target and maskers): 4000 Hz

  • Four maskers with time shifts: -24, -16, -8, +8 ms

  • Maskers nearly equally effective (iterative approach)

    • Amount of masking:  8 dB

  • Combinations: “M2-M3”, “M3-M4”, “M1-M2-M3”, “M2-M3-M4”, “M1-M2-M3-M4”

M1

M2

M3

T

M4

time (ms)

-24

0

-16

-8

+8

Δt


Waveform of four maskers at equally effective levels target at masked threshold for single masker
Waveform of four maskers at equally effective levels(target at masked threshold for single masker)

M4

T

M3

M2

M1


Additivity of temporal masking average results over five subjects

p << 0.05

p << 0.05

p << 0.05

p >> 0.05

Additivity of temporal maskingAverage results over five subjects

Error bars:

95% confidence

intervals

p >> 0.05

Empty symbols:

measured data

Filled symbols:

linear additivity model


Additivity of temporal masking average results over five subjects1
Additivity of temporal maskingAverage results over five subjects

Error bars:

95% confidence

intervals


Summary of temporal masking data average
Summary of temporal masking data(average)

  • No difference between forward and backward maskers

  • Amount of masking increases with number of maskers:

    • 2 maskers vs. 1 masker: + 18 dB (p << 0.05)

    • 3 maskers vs. 2 maskers: + 5 dB (p << 0.05)

    • 4 maskers vs. 3 maskers: + 11 dB (p << 0.05)

  • Amount of excess masking (nonlinear additivity) increases with number of maskers

    • 2 maskers: 14 dB

    • 3 maskers: 17 dB

    • 4 maskers: 26 dB

  • Results qualitatively consistent with literature data using stimuli with no or little temporal overlap of maskers


Additivity of frequency masking design
Additivity of frequency maskingDesign

  • Target frequency: 5611 Hz

  • Four simultaneous maskers with frequency separations: -7, -5, -3, +3 erbs

  • Maskers nearly equally effective

  • Amount of masking: 8 dB

  • Combinations: as for temporal masking

M1

M2

M3

T

M4

Frequency(erb)

-7

0

-5

-3

+3

Δf


Additivity of frequency masking design1
Additivity of frequency maskingDesign

  • Cochlear distortions (combination tones) could be detection cues

  • Therefore, lowpass-filtered background noise was added

  • The most critical condition (M3+T) wastested with/without noise on two subjects

  • No difference in threshold: so finally NO masking noise!


Additivity of frequency masking average results over five subjects
Additivity of frequency maskingAverage results over five subjects

Error bars:

95% CI

Empty symbols:

measured data

Filled symbols:

linear additivity model


Summary of frequency masking data average
Summary of frequency masking data(average)

  • Amount of masking depends on maskers involved:

    • M2-M3 vs. single: 3 dB (p < 0.05)

    • M3-M4 vs. single: 15 dB (p << 0.05)

    • M1-M2-M3 vs. M2-M3: 5 dB (p < 0.05)

    • M2-M3-M4 vs. M3-M4: 0 dB (p > 0.05)

    • M2-M3-M4 vs. M2-M3: 14 dB (p << 0.05)

    • M1-M2-M3-M4 vs. M1-M2-M3: 9 dB (p << 0.05)

    • M1-M2-M3-M4 vs. M2-M3-M4: 0 dB (p > 0.05)

  • Excess masking (nonlinear additivity) mainly occurring when higher-frequency masker (M4) included

    • Pairs: 2-3: 0 dB, 3-4:15 dB

    • Triples: 1-2-3: 5 dB, 2-3-4: 13 dB

    • Quadruple: 14 dB


Waveform of four maskers at equally effective levels(target at masked threshold for single masker)

M2

M3

M1

M4

T

Maskers M1,M2, and M3 overlap with each other, but not with M4


Discussion and conclusions
Discussion and Conclusions

  • Strong excess masking for Gaussian maskers if they are physically non-overlapping

  • Amount of excess masking increases monotonically with number of non-overlapping maskers

  • Excess masking is thought to be related to the compressivity of BM vibration (e.g. Humes and Jesteadt, 1989)

  • Thus, our Gaussians seem to be subject to BM compression, even though they are rather short (ERD = 1.7 ms)

  • This is consistent with the physiological finding that the BM starts to be highly compressive already 0.5 to 0.7 ms after the onset of a signal (Recio et al., 1998)


Modeling of results
Modeling of Results

Linear Energy Summation Model

  • Assumption: Masked threshold proportional to masker energy at out put of integrator stage

  • Combining two equally effective maskers A and B should produce X + 3 dB of masking

  • Valid for completely overlapping maskers

    Nonlinear Model

  • Assumption: Compressive nonlinearity in auditory system is preceding the integrator stage

  • Combining maskers A and B results in more than linear additivity (excess masking)

  • Valid for non-overlapping maskers


Modeling of results1
Modeling of Results

  • General form:

    where

  • MA, B: Amount of masking produced by maskers A or B

    MAB: Amount of masking produced by the combination of maskers A and B

    J: Compressive nonlinearity in peripheral auditory processing


Modeling of results2
Modeling of Results

  • Power-law model (Lutfi, 1980):

    • for p = 1: linear model

    • for p < 1: compressive model

      MTX: Masked threshold of masker X

  • Modified Power-law model (Humes et al., 1989):

    • Threshold in quiet (QT) considered as “internal noise”


Start with temporal masking perfect masker separation
Start with Temporal Masking: → perfect masker separation

  • Power Model:

  • best fit for p = 0.2

  • Mean error: 1.9 dB

  • Modified power model:

  • Prediction always too low


Include correction for quiet threshold 7 db
Include Correction for Quiet Threshold: -7 dB

  • Power Model:

  • Mean error: 1.9 dB

  • Modified power model:

  • Mean error: 1.6 dB

Why correction required?

→ Probably, absolute thresholds for Gaussians are no good approximation for internal noise


Spectral masking using same p value 0 2 and threshold correction
Spectral Masking Threshold: -7 dB: Using same p-value (0.2) and threshold correction

  • Power Model:

  • Good fit only for M3M4 (non-overlapping)

  • Modified power model:

  • too high predictions

Adjustment of parameters required!


P values optimized for modified power model
p-values optimized for Threshold: -7 dBModified Power model


Some questions
Some questions Threshold: -7 dB

  • Can we derive appropriate p-values from amount of overlap between maskers?

  • Can the (modified) power model be included into the Gabor-Multiplier framework to predict time-frequency masking effects for complex signals?




Acknowledgements
Acknowledgements Threshold: -7 dB

  • We would like to thank

    • the subjects for their patience

    • Piotr Majdak for providing support in the development of the software for the experiments

  • Work partly supported by WTZ (project AMADEUS) and WWTF (project MULAC)


End of talk Threshold: -7 dB


P values optimized for power model
p-values optimized Threshold: -7 dBfor Power model


Time frequency conditions
Time-frequency conditions Threshold: -7 dB

frequency

time


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