Transcript Research work on PERFORMANCE IMPROVEMENTS IN …

by
B.K.Sujatha
M.S. Ramaiah Institute of Technology, Bangalore
Guide:
Co-Guide:
Prof. Dr. P.S. Satyanarayana
Prof.Dr. K. N. Haribhat
The Head, Dept of Electronics &Comm Engg
B.M. Sreenivasaih College of Engineering
Bangalore
The Head, Dept of Telecomm Engg
Nagarjuna College of Engg and Tech
Bangalore
Topics
 SPEECH CODING
 DISCRIPTION OF EACH CODING TECHNIQUE
 ADAPTIVE DELTA MODULATION
 PRE-PROCESSING
 STEP SIZE ALGORITHM
 EXISTING STEP SIZE ALGORITHMS
 SONG ALGORITHM
 MODIFIED ABATE ALGORITHM
 PROPOSED ALGORITHM
 CONCLUSION
 BIBLIOGRAPHY
ADVANTAGES AND DIS-ADVANTAGES
OF DIGITAL COMMUNICATION
ADVANTAGES:
• Less distortion in the received signal.
• Simple and less expensive digital circuitry.
• Possibility of processing digital signals.
• Better received speech quality.
• Possibility of transmission of voice,video,data all in digital form.
• Possibility of correction of medium errors.
• Encryption/decryption for message security.
DIS-ADVANTAGES:
• Increased bandwidth.
• Synchronization requirement
.
SPEECH CODING
Conversion of analog speech signals into digital form
Types of speech coding:
•
•
•
•
Pulse Code Modulation
Differential Pulse Code Modulation(DPCM)
Delta Modulation(DM)
Adaptive Delta Modulation(ADM)
PULSE CODE MODULATION
Steps involved in PCM :
• Sampling
• Quantizing
• Encoding  n = log2L
Bandwidth of PCM depends on bit rate, R = nfs
For no aliasing,
fs >= 2 fm
BPCM >= ½ R = ½ nfs
DIFFERENTIAL PULSE CODE
MODULATION
• To minimize redundant transmission
• To reduce the bandwidth in comparison with PCM
DELTA MODULATION
ONE BIT OR TWO LEVEL VERSION OF DPCM:
This one-bit codeword eliminates the need for' word
framing’ at the transmitter & receiver & makes DM
systems very attractive for many classes of digital
communications.
NOISE IN DM :
• Smaller step size causes slope overload
distortion.
• Larger step size causes granular noise.
DM WAVEFORMS
LIMITATIONS OF DM
Slope overload (positive)
Max slope
overload
m(t)
^
stair case approximate m(t)
LIMITATIONS OF DM (contd..)
Slope overload (negative)
m(t)
^
m(t)
Negative slope overload
LIMITATIONS OF DM (contd..)
Granular noise
(slow varying signal)
^
m(t)
m(t)
ADAPTIVE DELTA MODULATION
• Improved version of DM by making the step size of
the modulator assume a time varying form.
• Here the step size is adapted to the level of the
input signal
ADM WAVEFORMS
Sample speech signal
 The sample speech waveform in the illustration is
taken from the speech sound “i i i i i” which is shown
in Figure. It is one of the waveforms used repeatedly in
the simulation that is about 5s long.
Pre-Processing
 A methodology for further improving the ADM
performance by pre-processing the speech signal prior
to the adaptation is presented.
 The
large variations in the speech are
removed/smoothened by a suitable pre-processing
method, one of which is using an integrator which can
smoothen the rapid changes.
 At the receiver, the differentiator is followed by a low
pass filter(LPF).
PRE-PROCESSING OF MESSAGE SIGNAL
m(t)
m(t)
(1)
(1)
(2)
(2)
t
(1)Slope
overload
distorti
(1)Slope overload
distortion
region
n region (2)Granu
(2)Granular noise
 m(t)dt  smoothes out m(t) , rapid changes may disappear.
t
Frequency response of Pre-Processor
(Integrator) at the transmitter
Frequency response of the
Differentiator at the receiver
The block diagram of Conventional ADM
ENCODER
DECODER
The block diagram of proposed ADM
ENCODER
DECODER
STEP-SIZE ALGORITHM:
• In the step-size algorithm, the processor detects the
pattern of e(t) where
^
e(t) = sgn [m(t)-m(t)]
• To see if the delta modulator is operating in the
quantization region, in which case e(t) produces an
alternating …1010… pattern, or in the slope overload
region in which case e(t) produces an all 1’s or all 0’s
pattern. These cases are illustrated as shown.
• If ADM senses a 1010 pattern, it decreases the step size,
and if it senses …1111… or …0000…, it increases the step
size . The manner in which the step size is altered
determines the algorithm.
Linear delta modulation and the bit
pattern produced for each region
m(t)
^
m(t)
t
e(k)
1 01 0 1 0 1 1 1 1 1 1
EXISTING STEP SIZE ADAPTATIONS
SONG ALGORITHM
• Here, we see that as long as e(k) is of the same sign as e(k-
1), the magnitude of the new step size s(k+1) will exceed
the magnitude of the old step size s(k) by so, the ‘min step
size’.
• However, if e(k) and e(k-1) differ in sign , the magnitude of
s(k+1) will be less than the magnitude of s(k) by the
amount so.
•
The equation describing the song algorithm is given by
│s(k)│+ so,
e(k) = e(k-1)
│s(k) │- so ,
e(k)  e(k-1)
|s(k+1)|=
MODIFIED ABATE ALGORITHM
• The need to maintain voice communications
as long as possible was a key factor in the selection
of the modified abate algorithm.
• The equation describing modified abate algorithm
is
[|S(k)| + So] e(k); e(k)=e(k-1)
and S(k) < 8So
S(k+1)=
|S(k)| e(k); e(k)=e(k-1)
and S(k) = 8So
So e(k);
otherwise
The Proposed step-size adaptation
 The new proposed technique for the step-size adaptation is described
as
[|S(k)|+S0]e(k); e(k)=e(k-1)
S(k+1)=
[β|S(k)|-S0]e(k);
S0e(k) );
e(k)≠e(k-1)
and β| S(k)|> S0
e(k)≠e(k-1)
and β| S(k)|< S0

is the adaptation parameter, nearly equal to 1 but, greater than 1.
β
1/()
The Proposed step-size adaptation
(cont…)
 This adaptation parameter  gives a better
performance to slope overload
 The parameter β takes care of the granular noise
as a result of which a better performance is
obtained as compared to SONG and modified
ABATE algorithms.
 Where  is taken as 1.1 and S0 as equal to 0.1.
SIMULATION
 SNR CALCULATION

{Xn }
^
→ samples of original signal (speech signal)
→ samples of final reconstructed signal
 {Xn }
^
 (Xn - Xn ) → error signal
^
 (Xn -X n )2 → squared error signal
where N is the total sample number of the input.
OR
(a)
(b)
(a) Performance Comparison of the proposed step-size adaptation
algorithm with the SONG and the modified ABATE algorithms.
(b) (b) the same plot of figure.(a) is shown but the input strength is
displayed for -7db to -1db.
(b)
(a)Performance Comparison of the proposed ADM with the SONG,
modified ABATE and the proposed algorithms.
(b) the same plot of figure.(a) is shown but the input strength is
displayed for -7db to -1db.
(a)
CONCLUSION
 Simulations are carried out for all the schemes. S0 is taken
as 0.1 and Simulations have also confirmed that with the
input strength for -7db to -1db on an average a 1.1dB
performance gain in the SNR is got for the new step-size
adaptation algorithm compared to the SONG and a 1.5dB
performance gain compared to the modified ABATE
algorithm.
 Next, with the proposed methodology(pre-processing)
and with the same input strength, on an average there is
1.4dB performance improvement in the SNR for the new
step-size adaptation algorithm as compared to the SONG
and a 1.7dB compared to the modified ABATE algorithm.
References
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[1] U N Chong Kwan, Hwang Soo Lee, “A study of the comparative performance of Adaptive Delta
Modulation systems”, IEEE Transaction vol 28, Jan 1980.
[2] Donald L, Schilling, Joseph gorodnick and Harold A.Vang, “Voice encoding for the space shuttle using
ADM” IEEE Trans. on Communications, vol. COM-26, no. 11, Nov 1982.
[3]. G. S. Tombras and C. A. Karybakas, “New adaptation algorithm for a two-digit adaptive delta
modulation system,” International Journal of Electronics, vol. 68, no.3, pp.343–349, 1990.
[4] M. A. Aldajani and A. H. Sayed, “A stable adaptive structure for delta modulation with improved
performance”, Proc. of IEEE International Conference on Acoustics, Speech and Signal processing
(ICASSP ’01), vol.4, pp 2621-2624, Salt Lake city, Utah, USA, May 2001.
[5] M. A. Aldajani and A. H. Sayed, “Stability and performance analysis of an adaptive sigma-delta
modulator,” IEEE Transactions on Circuits and Systems II, vol. 48, no. 3, pp. 233–244, March 2001
[6] K.Yao, K K Paliwal and S.Nakamura, “Noise adaptive speech recognition with acoustic models trained
from noisy speech evaluated on Aurora-2 database”, Proc. Intern. Conf. Spoken Language Processing,
Denver, Colorado, USA, pp.2437-2440, Sep.2002.
[7] Ming Yang, “Low bit rate speech coding”, Potentials, IEEE, vol 23, No. 4, pp. 32-36, Oct-Nov. 2004.
[8]Gibson J D, “Speech coding methods, standards and applications”, Circuits and Systems Magazine,
IEEE, Vol. 5, No. 4, Pp. 30-49, fourth Quarter 2005. .
[9] Hui Dong, Gibson J D, “Structures for SNR scalable speech coding”, IEEE Transactions on Audio,
Speech and Language Processing, vol. 14, No.2, pp.545-557, March 2006
[10] E. A. Prosalentis and G. S. Tombras, “A 2-bit adaptive delta modulation system with improved
performance,” EURASIP Journal on Advances in Signal Processing, vol. 2008, Article 9, 5 pages, 2008.