speech measurement using laser doppler vibrometer

SPEECH MEASUREMENT USING LASER DOPPLER VIBROMETER

SENSOR: APPLICATION TO SPEECH ENHANCEMENT

Presented By:Shamil. C

Roll no: 68E.I

Guided By:Asif AliLecturer in E.I

Introduction Speech measurement with LDV Principe of LDV Measurement Setup Problem formulation Speech Enhancement Algorithm Speckle noise suppression LDV-Based time frequency VAD Spectral gain modification Experimental Results Conclusion

CONTENTS

Achieving high speech intelligibility in noisy environments is one of the most challenging and important problems for existing speech-enhancement and speech-recognition systems.

Recently, several approaches have been proposed that make use of auxiliary non acoustic sensors, such as bone and throat- microphones.

Major drawback of most existing sensors is the requirement for a physical contact between the sensor and the speaker.

Here present an alternative approach that enables a remote measurement of speech, using an auxiliary laser Doppler vibrometer (LDV) sensor.

INTRODUCTION

SPEECH MEASUREMENT WITH LDV

fd(t) = 2ν(t) cos(α)/λ ν(t)=> instantaneous throat-vibrational velocityα => Angle between the object beam and the

velocity vectorλ =>laser wavelength.

LDV-output signal after an FM-demodulator is Z(t) = fb + [2Av cos(α)/λ].cos(2πfvt). (1)

LDV Output signal

MEASUREMENT SETUP

Employing the VibroMet™500V LDV. Consists of a remote laser-sensor head

and an electronic controller. Operates at 780 nm wavelength. Can detect vibration frequencies from DC

to over 40 kHz. Its operational working distance ranges

from 1 cm to 5 m.

let y(n) =x(n) + d(n) y(n)-observed signal in the acoustic sensor. x(n) -Speech signal. d(n)-Un correlated additive noise signal. In the STFT domain, Ylk = Xlk + Dlk

Where l= 0, 1, . . . is the frame index. k = 0, 1, . . . , N − 1is the frequency-

bin index.

PROBLEM FORMULATION

Use overlapping frames of N samples with a framing-step of M samples.

Let H0lk and H1lk indicate, respectively, speech absence and presence hypotheses in the time-frequency bin (l, k), i.e.,

H0lk: Ylk = Dlk

H1lk: Ylk = Xlk + Dlk.

XL lk = GlkYlk.

The OM-LSA estimator minimizes the log spectral amplitude under signal presence uncertainty resulting in,

Glk = {GH1lk}ˆPlk.Gminˆ1−Plk .Where, GH1lk is a conditional gain function given H1lk &

Gmin<< 1 is a constant attenuation factor.

Plk is the conditional speech presence probability.

Denoting by ξlk and γlk we get,

is the a priori probability for speech absence,

-Posteriori SNR

-Priori SNR

Speckle-Noise Suppression The output of the speckle-noise detector is,

Wl(n) = Gl Zl(n) Where Gl= Gsmin<<1 for Il = 1(speckle noise is

present) Gl = 1 otherwise.

SPEECH ENHANCEMENT ALGORITHM

Resulting signal after applying speckle reduction algorithm

LDV Based time frequency VAD

-Represents the noise-estimate bias

-Smoothed-version of the power spectrum

Then, we propose the following soft-decision VAD:

Spectral Gain Modification

Speech in a given frame is defined by

We attenuate high-energy transient components to the level of the stationary background noise by updating the gain floor to

-Stationary noise-spectrum estimate

-Smoothed noisy spectrum

EXPERIMENTAL RESULTS

Speckle noise was successfully attenuated from the LDV-measured signal using a kurtosis-based decision rule.

A soft-decision VAD was derived in the time-frequency domain and the gain function of the OM-LSA algorithm was appropriately modified.

The effectiveness of the proposed approach in suppressing highly non-stationary noise components was demonstrated.

CONCLUSIONS

I. Cohen and B. Berdugo, “Speech enhancement for nonstationary noise environment,” Signal Process., vol. 81

T. F. Quatieri, K. Brady, D. Messing, J. P. Campbell, W. M. Campbell, M. S. Brandstein, C. J.Weinstein, J. D. Tardelli, and P. D. Gatewood, “Exploiting nonacoustic sensors for speech encoding,”

T. Dekens, W. Verhelst, F. Capman, and F. Beaugendre, “Improved speech recognition in noisy environments by using a throat microphone for accurate voicing detection,” in 18th European Signal Processing Conf. (EUSIPCO), Aallborg, Denmark, Aug. 2010, pp. 23–27

M. Johansmann, G. Siegmund, and M. Pineda, “Targeting the limits of laser doppler vibrometry,”

http://www.metrolaserinc.com

REFERENCES

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