This document discusses using a laser Doppler vibrometer (LDV) sensor to remotely measure speech for speech enhancement in noisy environments. It presents a speech enhancement algorithm that uses the LDV measurements along with acoustic sensor measurements. The algorithm includes speckle noise suppression of the LDV signal, an LDV-based time-frequency voice activity detection, and spectral gain modification. Experimental results demonstrate the effectiveness of the proposed approach in suppressing highly non-stationary noise components.

1. Guided By: Presented By:
Asif Ali Shamil. C
Lecturer in E.I Roll no: 68
E.I

2.  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

3.  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.

5.  fd(t) = 2ν(t) cos(α)/λ
ν(t)=> instantaneous throat-vibrational
velocity
α => Angle between the object beam and the
velocity vector
λ =>laser wavelength.
 LDV-output signal after an FM-demodulator
is
Z(t) = fb + [2Av cos(α)/λ].cos(2πfvt). (1)

7. 





9.  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.

10. 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.
X̂lk = GlkYlk.

11. 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.

12.  Denoting by ξlk and γlk we
get,
-Priori SNR
-Posteriori SNR
is the a priori probability for speech
absence,

13.  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.

15. -Represents the noise-estimate bias
-Smoothed-version of the power spectrum
Then, we propose the following soft-
decision VAD:

17. 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

19.  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.

20.  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