2 © Nokia Siemens Networks Network VQE/ THEPIE FAPI ...departements.imt-atlantique.fr/data/sc/seminaires/seminaire_thepie... · 4 © Nokia Siemens Networks Network VQE/ THEPIE FAPI

2 © Nokia Siemens Networks Network VQE/ THEPIE FAPI/ October 2007

Network Voice Quality Enhancement Through LPC Parameters

Modifications:’’ Noise Reduction Within Network through Modification of the

LPC Parameters’’, 7th ITG-SCC 2008.

October 2007

Emmanuel THEPIE FAPI


Outline

� Introduction.

� Network Speech Enhancement: The Concept.

� CELP Overview.

� LPC Analysis: The LPC coefficients.

� Noise Reduction through LPC coefficients.

� Voice Activity Detector (VAD).

� Experiments Results.

� Simulation Results.

� Conclusion.


Introduction

Voice Quality Enhancement (VQE) algorithms now appear as solution to improve speech quality whithin network.

Classical Algorithms: decoding of the bitstream, performing the enhancement on the decoded signal and re-encoding the processed speech signal.

Consequence:computational expensive, additive delay and reduction of the speech quality due to tandeming effect.

Alternative Approach: modification of the bitstream itself to avoid tandeming effect.


Network Speech Enhancement: General Concept

VQE could be made directly on the available bitstream, avoid decoding/coding process necessary in classical solution.

CELP Encoder

Noisy Speech

y(n)

Noisy Parameters ExtractionNoisy

bitstream

Modification of the Noisy

Parameters

OtherParameters

Reconstruction of the Whole

Bitstream

Modified bitstream CELP

Decoder

Network AreaFar-End Speaker side Near-End Speaker side

y´(n)

Codec Domain Noise Reduction


CELP Codec Overview

Block by block speech processing with frame of 20ms length, each frame divided into 4 subframes.

Encoder:

LPC analysis: LPC analysis filter (LPC coefficients, order M = 10)> LSP> LSF.

Adaptive Search: Adaptive excitation (Pitch delay and Adaptive Gain).

Fixed Codebook Search: Fixed Excitation and Fixed Gain.

Decoder:

Speech synthesis using the transmitted parameters. Adaptive excitation added to Fixed excitation and enter the LPC synthesis filter (inverse of the LPC filter ).

Post-processing Algorithm applied to enhanced the quality of the reconstructed speech.


CELP Codec Overview: Decoder

v(n), Adaptive Codebook

Short-Term Synthesis

Filter

c(n), Fixed Codebook

Post Processing

gc

Reconstructed Speech

ga

Index (fixed & adaptive) LPC Coefficients

Binary Stream

Excitation Parameters (Index and Gains) Spectral Parameters: LSF Indices L0, L1, L2, L3


LPC Analysis

Problem: Given a sample set of stationary processes, how to predict the value of the processes in the future.

LPC Analysis Solution: In a Pth order LPC analysis, the present sample can be estimated as a linear combination of P past samples.

n

P

k

ks eknsans +−⋅−= ∑

=1

)( )()(

ne

)( ksa

: is the prediction error or residual signal and,

: are the linear prediction coefficients or LPC coefficients andP is the order of the prediction.

Application: Method of removing redundancy in a signal.


LPC Analysis (2)

AMR LP analysis block

Windowing

and autocorrelation

Levinson Durbin

Algorithm

A(z) → LSP

LSF Indices L0, L1, L2, L3

LSP → A(z)

LSP Quantization

LSP → A(z)

A(z) Â(z)

Preprocessed speech s(n)

Reflection Coefficients k

The optimum LPC coefficients are obtained when the error energy is minimal. The minimum of this energy occurs when the derivative is zero with respect to each LPC coefficients.

∑ ∑−

= =

−⋅+=1

0

2

1

)( )()(N

n

P

k

ksST knsansE


LPC Analysis in Noisy Envionment

In this section , we consider a noisy speech signal as: )()()( npnsny +=

where s(n) is the clean speech and p(n) the noise or perturbation.

Using this signal, and applying the linear prediction analysis, the sum square of the prediction error is given by the relation:

∑ ∑−

= =

−+−⋅++=1

0

2

1

)( )]()([)]()([N

n

P

k

kyST knpknsanpnsE

The minimum of this energy occurs when the derivative is zero with respect to each noisy LPC coefficients.


LPC Analysis in Noisy Envionment (2)

0)]()()()()()()()([

)()()()()()()()(

0))()())](()(())()([(20

1

0 1

)(

1

0

1

0 1

)()(

=−−+−−+−−+−−⋅

+−+−+−+−⇔

=

−+−−+−⋅++⇔=∂∂

∑∑

∑

∑ ∑

−

= =

−

=

−

= =

jnsknpjnpknsjnpknpjnsknsa

jnsnpjnpnsjnpnpjnsns

jnpjnsknpknsanpnsa

E

N

n

P

k

ky

N

n

N

n

P

k

kyj

y

ST

As the clean speech signal and the noise are non correlated, the equation is reduced to:

( ) )]()()()([)()()()(1

0 1

)(1

0

jnpknpjnsknsajnpnpjnsnsN

n

P

k

ky

N

n

−−+−−⋅−=−+− ∑∑∑−

= =

−

=



∑ ∑∑∑∑=

−

=

−

= =

−

=

−−⋅−=−−⋅−=−P

k

N

n

ks

N

n

P

k

ks

N

n

jnsknsajnsknsajnsns1

1

0

)(1

0 1

)(1

0

)()()()()()(

- When we encode the clean speech, its LPC coefficients verify:

∑ ∑∑∑∑=

−

=

−

= =

−

=

−−⋅−=−−⋅−=−P

k

N

n

kp

N

n

P

k

kp

N

n

jnpknpajnpknpajnpnp1

1

0

)(1

0 1

)(1

0

)()()()()()(

- When we encode the noise signal, its LPC coefficients verify:



( ) )]()()()([)()()()(1

0 1

)(1

0

jnpknpjnsknsajnpnpjnsnsN

n

P

k

ky

N

n

−−+−−⋅−=−+− ∑∑∑−

= =

−

=

)]()()()([

)()()()(

1

0 1

)(

1

01

)(1

01

)(

jnpknpjnsknsa

jnpknpajnsknsa

N

n

P

k

ky

N

n

P

k

kp

N

n

P

k

ks

−−+−−⋅−

=−−−−−−

∑∑

∑∑∑∑−

= =

−

==

−

==

Replacing the corresponding terms in the noisy environment, the result is as follow:

∑ ∑

∑∑∑∑

=

−

=

−

==

−

==

−−+−−−

=−−−−−−

P

k

N

n

ky

N

n

P

k

kp

N

n

P

k

ks

jnpknpjnsknsa

jnpknpajnsknsa

1

1

0

)(

1

01

)(1

01

)(

)]()()()([

)()()()(

And finally:



ypskjnkinkjirN

nk ,,,)()()(

1

0

=−⋅−=− ∑−

=

ypskjir Pjikk ,,,))(( ,1 =−=Γ ≤≤

ypskinknkirN

nk ,,,)()()(

1

0

=−⋅=∑−

=

ypska Piti

kk ,,,)( ,,1)( ==Α = K

ypsppss Α⋅Γ+Γ=Α⋅Γ+Α⋅Γ )(

� The autocorrelation function values of windowed signals: y(n), s(n) or p(n).

� The autocorrelation Matrix: toeplitz structure.

� The LPC coefficients vector.

The problem can now be expressed as follow:

And:

)()( 1pypsys AA −⋅Γ⋅Γ+Α=Α − (*)


Noise Reduction Through LPC Coefficients

ENCODER

Noisy Speech Signal

Âs

Bitstream

Decoding of the noisy LPC coefficients

Other Parameters keep unchanged

Mapping Âs

In the bitstream

F

G Modified Bitstream

Pitch delay index, Adaptive Gain index, Fixed codebook index

Ay

Terminal Network Area

VAD 1

0

Noise Reduction using the VAD decision: General overview

� If VAD = 1, the modification function F is applied on the noisy LPC coefficients Ay to compute the estimated clean LPC coefficients Âs.

� If VAD = 0, Ay is passed through the whitening block G to giveÂs.


AMR Voice Activity Detector: Option 1

s(i)Filter Bank and Computation of sub-band levels

VAD Decisiont1, t2

Pitch Detection

Tone Detection

Complex Signal Analysis

Top(n)

Open-Loop Correlation

Vector

VAD_flag

Level[n]

Pitch

tone

Complex_Warning

Complex_Timer

Simplified block diagram of the VAD Algorithm option 1.


LPC Enhancement when VAD = 0: G

If VAD = 0, (*) cannot be used to estimate the clean LPC coefficients. We introduce here the Spectral Dampingof the noisy LPC which characterize the filter G.

The spectral damping is achieved by applying in the Z-domain an homothety with center at the origin and a ration λ > 1, so that Ay(z) is modified according to:

)()(

1)(ˆ)()(ˆ kaka

zAzA yksys ⋅=⇒=

λλ

Consequence:if λ is significantly high, the transformation leads to an attenuation and a whitening of the noisy LPC coefficients.


LPC Enhancement when VAD = 0: G (2)

The ratio λ in each subframe m is computed as a linear function of the noise signal energy Êp(m).The variation of the noise spectrum is taken into account.

ϕνλ +⋅= )(ˆ)( mEm p

Êp(m)

Tmin Tmax

λmin

λmax

λ(m)

Evolution of the damping factor.0 500 1000 1500 2000 2500 3000 3500 4000

-15

-10

-5

0

5

10

15

20

Frequency Bins

Am

plit

ud

e

Clean S pectrum

Es timated S pectrumNois y S pectrum

Example of spectral damping


LPC Enhancement when VAD = 1

)ˆ(ˆ)ˆ(ˆ 1pypsys AA −⋅Γ⋅Γ+Α=Α −

In this formula only the noisy LPC coefficients Ay are available, but the remaining parameters need to be estimated i.e.:

� The noise signal LPC coefficients: � The noise signal Autocorrelation Matrix:� The Clean speech Autocorrelation Matrix:


Noise Signal LPC coefficients

)1(ˆ)(ˆ,1

)()1()1(ˆ)(ˆ,0

−==

⋅−+−⋅==

mAmAVADif

mAmAmAVADif

pp

ypp αα




Noise Signal Autocorrelation

Matrix

The Autocorrelation vector can be reconstructed given the set of noise LPC coefficients (already estimated) and its associated prediction error power. It is achieved using the Inverse Recursive Levinson-Durbin algorithm (ILD).

If Py(m) is the noisy error power, the noise signal prediction error power is given by:

)1(ˆ)(ˆ,1

)()1()1(ˆ)(ˆ,0

−==

⋅−+−⋅==

mPmPVADif

mPmPmPVADif

pp

ypp µµ

))(ˆ),(ˆ()(ˆ mPmAILDmR ppp =Finally:




Clean Speech Autocorrelation

Matrix

The clean speech autocorrelation vector is computed taking into account that s(n)and p(n)are not correlated:

)(ˆ)(ˆ)(ˆ)(ˆ)()(ˆ mmmmRmRmR pyspys Γ−Γ=Γ⇒−=

Simulations show that the estimation of the clean speech autocorrelation matrix is source of unstability problem of the associated synthesis filter. To overcome this problem, we study two tools: a post-filtering and a threshold noisy energy test.


LPC Enhancement when VAD = 1 : Post-filtering

)1()(

)()(

)1()(

)()(

1))((

τ

τ

−⋅=

−⋅=

>

poabs

poimagpoimag

poabs

porealporeal

poabsif

84.01 =−τ

� If the pole po is out of the unit circle then the algorithm brings back the pole inside the unit circle. The real and imagine parts of the pole are transformed based on the following Algorithm:

� In term of poles, a filter is stable if and only if all the poles are inside the unit circle in the z-plane domain. If there is an observable pole outside the unit circle, then there is an exponential increasing component of the impulse response.


Noisy poles Enhanced poles

Clean LPC poles

LPC Enhancement when VAD = 1 : Post-filtering


Experimental Results

� Comparison of LPC coefficients (our method against Wiener filter method) in frequency domain under several noise files during speech activity.

� Analyzing of the enhanced speech signals in frequency representation.

AMR Encoder

Noisy Speech

y(n)

Noisy Speech

y(n)

Clean Speech

s(n)

AMR Encoder

Wiener Filter (NR)

AMR Encoder

Modification of the noisy LPC

Spectral

Errors of the LPC Analysis: Comparison of Hu=FFT{1/A u(z)}

u € {y, s, ŝ, wiener}

Ay

As

AWiener

Âs

System of the Spectrum Analysis


Experimental Results

� Comparison of LPC coefficients (our method against Wiener filter method) in frequency domain.

)(),(),(ˆ fHfHfH sys and )( fHWiener

{ }swieneryufHfHN

Errorf

usFFT

ˆ,,)()(1 ∈−⋅= ∑

( )( )∑

∑∑ −

=

−

=−

= +

+⋅=⋅= 1

0

2

1

0

2

10

1

0 )(

)(log10)(,)(

1N

n

N

nL

lseg

nlNp

nlNslSNRlSNR

LSNR

The spectral error is computed as follows:

The SNR over sub-frame with VAD = 1 are given by:

))(

1()(

zAFFTfH

uu =Where : { }ywienerssu ,,ˆ,∈


Overview on Wiener Filter Noise Reduction

With this method, we decode the noisy signal, we apply the Wiener filter and we re-encode the enhanced signal.Short time frequency analysis of the decoded noisy speech signal: Yf(m).The estimated clean speech is obtained by weighting Yf(m) for each frequencyf with a factor Gf(m).

)()()(ˆ mYmGmS fff ⋅=)(1

)()(

mSNR

mSNRmG

f

ff +

=and the Wiener filter is given by:

The signal to noise ration in frequency domain SNRf(m) is computed as follows:

)()1()(ˆ

)1(ˆ)(ˆ

,,

2

mSNRm

mRNSmRNS fpost

fN

f

f ⋅−+−

⋅= βγ

β 1)(ˆ

)()(

,

2

, −=m

mYmSNR

fN

f

fpost γ

)1(ˆ)1()()(ˆ ,

2

, −⋅−+⋅= mmYm fNffN γδδγ

with

The noise PSDis estimated by:


Simulation Results

0 500 1000 1500 2000 2500 3000 3500 4000−20

−10

0

10

20

30

Frequencies bin

Am

plitu

de in

dB

Speech spectrumEstimated spectrum: 0.5dB ErrorNoisy spectrum: 1.12dB ErrorWiener spectrum: 6.6dB Error

0 500 1000 1500 2000 2500 3000 3500 4000

−15

−10

−5

0

5

10

15

20

25

30

35

Frequencies bin

Am

plitu

de in

dB

Speech spectrumEstimated spectrum: 1.3dB ErrorNoisy spectrum: 3dB ErrorWiener spectrum: 4.7dB Error

Example of typical spectrums, car noise SNRseg= 5dB


Simulation Results (2)

0 500 1000 1500 2000 2500 3000 3500 4000

−15

−10

−5

0

5

10

15

20

25

30

Frequencies bin

Am

plitu

de in

dB

Speech spectrumEstimated spectrum: 1dB ErrorNoisy spectrum: 1.9dB ErrorWiener spectrum: 3.6dB Error

Example of typical spectrums, car noise SNRseg = 10dB



Fre

quen

cy (

Hz)

0 1 2 3 4 5 6 7 8 90

2000

4000

20 40 60 80 100 120

0 1 2 3 4 5 6 7 8 9

−5000

0

5000

Time (sec)

Am

plitu

de

Noisy Speech Spectrogram SNRseg= 10



Fre

quen

cy (

Hz)

0 1 2 3 4 5 6 7 8 90

2000

4000

20 40 60 80 100 120

0 1 2 3 4 5 6 7 8 9

−5000

0

5000

Time (sec)

Am

plitu

de

Wiener Method Spectrogram SNRseg= 10



Fre

quen

cy (

Hz)

0 1 2 3 4 5 6 7 8 90

2000

4000

20 40 60 80 100 120

0 1 2 3 4 5 6 7 8 9

−5000

0

5000

Time (sec)

Am

plitu

de

LPC method Spectrogram


0 200 400 600 800 1000 1200-50

-40

-30

-20

-10

0

10

20

30

S ignal Frames

Sig

na

l Po

we

r in

dB

ov

Nois y S ignal

Enhanced S ignalClean S ignal

Energy with Renault Car Noise: SNRseg= 5dB

Typical Experiement Results


Typical Experiement Results

- 1.9- 0.43- 0.1DSN

- 17.7- 10.8- 6.9NPLR

- 43.4- 37.7- 43.52TNLR

15.810.346.8SNRI

FG and LPC NRLPC Only NRFG Only NRMetrics

Typical Results of objective Measures


Conclusion

� Effectiveness of the noise reduction through modification of noisy speech LPC coefficients.� Solutions to stability problems:� We experiment that for a given subframe m, the filter F is applied if only if : VAD(m) = 1and Ey(m) > Emin.� Results from Peter Kabal, ’’Ill-Conditioning and Bandwidth Expansion in Linear Prediction of Speech,’’ show that stability failure can appear in a normal speech area and is directly relate to theautocorrelation matrix conditioning.� Enhancement of the complexity and avoiding the use of the VAD decision in PCM domain.� Combination with Fixed-Gain noise reduction ref[2].� Investigation to retain the optimum network architecture for implementation.� Integration to Smart Transcoding.


References

[1] – R. Chandran and D. J. Marchok, ’’Compressed domain noise reduction and echo suppression for network speech enhancement,’’ in Proc. of the 34rd IEEE Midwest Symposium on Circuit and Systems, 2000, vol. 1, pp. 10-13.

[2] – Taddei H., Beaugeant C., and De Meuleneire M., ’’Noise reduction on speech parameters,’’ in ICASSP, 2004.

[3] – Sukkar R. A, Younce R., and Zhang P., ‘‘Dynamic scaling of encoded speech through direct modification od coded parameters,‘‘ in ICASSP, 2006.

[4] – Pasanen A., ‘‘Coded domain level control for AMR speech codec,‘‘in ICASSP, 2006.

[5] – Gordy J. D., Goubran R. A., and Matthews M. B., ‘‘Reduced-delay mixing of compressed speech signals for VoIP and cellular telephony,‘‘ in Asilomar Conf. on Signal System and Computers, 2004, vol. 2, pp. 2270-2274.

[6] – Un, C. and Choi K., ‘‘Improving LPC analysis of noisy speech by autocorrelation subtraction method,‘‘ in ICASSP, 1981, vol.6, pp. 1082-1085.


References (2)

[7] – 3GPP TS 26.071, Mandatory Speech Codec speech processing functions; AMR speech codec; General Description, June 2002.

[8] – Atal B. S. and Schroeder M. R., ‘‘Coded-excited linear prediction (CELP): High-quality speech at very low bit rates,‘‘ in ICASSP, 1985, pp. 937-940.

[9] – 3GPP TS 26.094, Adaptive Multi-Rate (AMR) speech coded, Voice Activity Detector (VAD), Release. 6, June 2006.

[10] – Simon Haykin, ‘‘Adaptive Filter Theory,‘‘ Prentice Hall Information and System Series, 2002.

[11] – El-Jaroudi, A. and Makhoul J., ‘‘Dicrete all-pole modeling,‘‘ in IEEE Trans. on signal Proc. 1991, vol. 39, pp. 411-423.

[12] – Ephraim Y. and Malah D., ‘‘Speech enhancementusing a minimum mean square error short time amplitude estimator,‘‘ in IEEE Trans. on ASSP, 1984, vol. 32, pp. 1109-1121.


References (3)

[13] – A. M. Kondoz, ’’Digital Speech Coding for Low Bit Rate Communication System,’’Wiley and Sons, 1994.

[14] – Peter Kabal, ’’Ill-Conditioning and Bandwidth Expansion in Linear Prediction of Speech,’’Proc. IEEE int. Conf. Acoustic, Speech, Signal Processing (Hong Kong), pp. I-824-I-827, April 2003.

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2 © Nokia Siemens Networks Network VQE/ THEPIE FAPI ...departements.imt-atlantique.fr/data/sc/seminaires/seminaire_thepie... · 4 © Nokia Siemens Networks Network VQE/ THEPIE FAPI