De-Reverberation Using LPC -...

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De-Reverberation Using LPC Presented by: Eyal Enav

Transcript of De-Reverberation Using LPC -...

Page 1: De-Reverberation Using LPC - Technionwebee.technion.ac.il/Sites/People/IsraelCohen/Info/2011/EyalEnav.pdf · Larynx Cycles are averaged (de-reverberation) Actual De-Reverberation.

De-Reverberation Using LPC

Presented by:Eyal Enav

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LPC temporal averaging basic concept

• LPC coefficients achieved by

• Estimation error = residual excitation.

• The spatial expectation of the LPC coefficients is not affected by reverberation.

• Applying the LPC filter to reverbed signal yields reverbed excitation.

ARMSE S S

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Single channel LPC

• Calculate the cross correlation vector .

• Calculate autocorrelation matrix .

• The LPC filter is

• The filter approximates the clean speech LPC in terms of spatial expectation:

xxR

xxr

1ˆxx xxb R r

ˆ ˆspatialE b a

1ˆss ssa R r

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Multichannel LPC

• For each channel calculate

• The LPC filter achieved by averaging:

• Approximates spatial averaging.

• Best results.

, ,,xx m xx mR r

1ˆxx xxb R r

,

1

1 M

xx xx m

m

R RM

,

1

1 M

xx xx m

m

r rM

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DSB- LPC

• Perform delay and sum beamformer.• Calculate LPC based on DSB output:

• LPC estimation error (Itakura distance) :– Increases with reverberation time. – Bad results

1

1 M

m m

m

x x nM

1ˆDSB xx xxb R r

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Z Plane MC/DSB LPC

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LPC-Single/Multi/DSB

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Extracting the reverbed excitation

• Theory:

If the LPC coefficients are identical to those of the clean speech signal. (Graph Itakura for MC)

Then applying the LPC filter to the reverbed signal results in the reverbed excitation:

is the Fourier transform of the excitation at the microphone.

is the Fourier transform of the RIR from the source to the microphone.

j j j

m mE e E e H e

mE thm

mH thm

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Algorithm

1. Multichannel LPC filter is calculated.

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Algorithm

2. LPC filter is applied on the DSB-output.

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Algorithm

3.Glottal Closure Instants locations are identified (complex algorithms)

6400 6600 6800 7000 7200 7400 7600

-0.005

0

0.005

0.01

0.015

0.02

0.025

GCI identification on LPC residual

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Algorithm

4. Larynx Cycles are averaged (de-reverberation)

Actual

De-Reverberation

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Larynx Cycles Averaging

• Presumption:There is low correlation between the reverb impulse response of the consecutive GCIs within consecutive larynx cycles.

Therefore averaging consecutive larynx cycles is expected to remove the unwanted reverb.

• In fact :– True only for large T60 > Larynx cycle length.– False for early reverberation

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Larynx Cycles Averaging

Ideal:• Perform averaging only of the reverb signal in

consecutive larynx cycles.• Keep the original glottal pulses.

Should be taken to consideration:• GCI’s estimation is imperfect.• The glottal pulse is spread in time.

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Larynx Cycles Averaging

Method :• Multiply each of the consecutive larynx cycles by

a Tukey window (W) and average.• Add to the average in order to keep

the original glottal pulses.

1

ˆ ( )2 1

K

vec j vec j vec j

i K

e n I W e n We n iK

( ) jI W e n

1 1e [ ... ]T

vec j j j j Ln e n e n e n

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Larynx Cycles Averaging

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Larynx Cycle Averaging

Symmetric Tukey window averaging ignores:• Short, early reverberations traces are more likely to

be correlated.(T60<Larynx cycle length)

• Reverb signal tends to decay with time.

• The glottal pulse is asymmetric.

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Use a window that uses prior information:• Biased Tukey window:

Emphasizing the early part of the larynx cycle.

• Glottal pulse shape oriented window:– Assuming GCI is precise.– Define a window the relates to the common shape

of a glottal pulse.

Suggested Improvement

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LPC dereverbration frameworkimplemented

• RIRs created using Emanuël Habets RIR generator.

• Ideal DSB employed using known source to microphones delays.

• Multi channel LPC employed.

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LPC dereverbration frameworkimplemented

• GCIs extracted from clean speech are employed for larynx cycle averaging.

• Larynx cycles are scaled to the center cycle size prior to averaging.(sync interpulation)

• Larynx cycle averaging using Tukey window.

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