A high security and noise immunity of speech based on double chaotic masking

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 4, August 2020, pp. 4270~4278
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i4.pp4270-4278  4270
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
A high security and noise immunity of speech based
on double chaotic masking
Ehab AbdulRazzaq Hussein1
, Murtadha K. Khashan2
, Ameer K. Jawad3
1,2
Department of Electrical Engineering, Faculty of Engineering, University of Babylon, Iraq
3
Department of Computer Engineering, Faculty of Engineering, University of Islamic, Iraq
Article Info ABSTRACT
Article history:
Received Jul 11, 2019
Revised Feb 25, 2020
Accepted Mar 2, 2020
It is known that increasing the security of the information and reducing
the noise effect through public channels are two of the main priorities in
developing any communication system. In this article, an efficient, secure
communication system with two levels of encryption has been applied to
the speech signal. The suggested security approach was implemented by
using two different stages of chaotic masking on the signal; one masking was
conducted by using Lorenz system and the other masking was built by using
Rӧssler chaotic flow system. The main goal of developing this two-chaotic
masking approach is to increase the key space and the security of
the information. Also, an immunity technique has been implemented in
the suggested approach to reduce the noise effect. For practical application
purposes, this system was tested with additive white gaussian noise (AWGN)
channel. The simulation results show that the quality of reconstructed speech
signal is changeable according to the used signal to noise ratio (SNR);
therefore, a proposed technique based on digital processing method (DPM)
was applied to the first masked signal by converting the sampled data from
the analog to the binary format. The simulation results show that an 22 dB
(SNR) is sufficient to recover the speech signal with minimum noise by
using the suggested approach.
Keywords:
Chaotic flow systems
Double chaotic masking
communication security
Encryption
Speech quality
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ehab AbdulRazzaq Hussein,
Department of Electrical Engineering,
Faculty of Engineering,
University of Babylon, Babylon, Iraq.
Email: dr.ehab@itnet.uobabylon.edu.iq
1. INTRODUCTION
In communication system security is the main goal that must be achieved, in order to keep data
away from attacks (an eavesdropper). Information must be encrypted over various techniques,
Send encrypted speech or any form of data need to use a general key that should be known by the receiver of
communication system. A chaotic system has many properties like, very sensitive to initial condition and
parameters, ergodic, unpredictable signal and deterministic system. Due to the important properties of chaotic
that mentioned, chaotic can be utilized in secure communication.
Several works and study can be done in the application of chaotic in secure communication.
Since 1993 Oppenheim and Cuomo, implemented masking scheme of the Lorenz system in secure
communication [1, 2]. In 1996 Milanovice Zaghloul put an improved technique to implement chaotic
masking in the communication field [3, 4]. After this chaotic circuit of secure communication based on
Lorenz system is implemented in discrete electronics component [5, 6]. In 2009 a secure communication
based on a fractional chaotic method was proposed [7]. In 2011, a new 3-dimensional system based on chaos
theory was proposed by Li and Ou [8], using the chaotic flow, Lorenz system. Zhang and Zhao have studied
a two-stage driver system by employing the pecora and carroll (PC) method on the Lorenz chaos model in

Int J Elec & Comp Eng ISSN: 2088-8708 
A high security and noise immunity of speech based … (Ehab AbdulRazzaq Hussein)
4271
the field of secure communications [9]. In 2018, presented a security of speech based on two encryption
stage, the first stage is time scrambling to construct by chaotic map, while the second stage is chaotic
masking established by the chaotic flow [10].
Yamada and Fujisaka [11], conducted on the first research on synchronizing, a chaotic system.
While pecora carroll [12, 13] achieve that chaotic models can be synchronized when a suitable connection
design is found, such as the chaotic time growth of the two- systems become comparable. Chaotic secure
communication is presented in three main types: Chaotic modulation, chaotic masking and chaotic shift
keying [14, 15]. In this work two levels of chaotic masking are applied to the speech signal.
The outline of this article is as go ahead. In Section 2, we set the description of the proposed system;
the aim of the multi chaotic masking; the design of the proposed system with additive white gaussian noise
(AWGN) channel has been introduced. In Section 3, we expose the quality measurement of the speech so as
to explain the robustness of the model that studied concerning the residual clarity of the ciphered signal and
the properties of the received data. In Section 4 the simulation results are obtained include the masking/de-
masking process and the results of using the proposed method to reduce the effect of noise. The conclusion is
presented in the last section.
2. THE PROPOSED MODEL
The security of the encrypted signal increases proportional to the randomness of the chaotic signal
and the size of the key space of the system that be used. In this work double masking to the speech signal
based on chaotic signal has been introduced. Each part of the proposed system needs to be synchronized with
their identical parts at the receiver side. As a result, self-synchronization (PC) method that proposed by
Pecora Carroll has been used in this model, in which two identical systems, one master system (driven) and
other slave system (response) can be synchronized, by choosing one of the states of the differential equations
of master system as a driven signal to the other side slave system [12]. Lorenz and Rӧssler consist, the first
and the second stages of the proposed model, respectively as clarify in Figure 1. Then the noise effect on the
received data has been introduced by applying an AWGN channel to the system, so that a specific method is
proposed to reduce noise on the signal by digital processing method to the first chaotic masking.
The proposed model components can divided as the following:
Figure 1. General diagram of proposed model
2.1. Double masking secure communication
There are several applications to the chaotic signal in secure communications such as the parameter
chaotic modulation, the chaotic shift keying, the on- off keying, and the chaotic masking [16]. The chaotic
masking can be considered the simplest application for the chaotic signal in the secure communications.
Also, it can be easily executed in the electronic circuits, so it is used in this proposed system. The schematic
of the double masking for the proposed system used in this study is shown in Figure 2.
At transmitter side. In the first block, the Lorenz flow system is used [17], since we have three states
in the system (ẋ , ẏ , ż) and according to self-synchronization, by choice xm1 as a chaotic mask to the data,
as follow. The Lorenz master system is given by:
ẋm1 = σ(ym1 − xm1)
ẏm1 = rxm1 − ym1 − xm1zm1 (1)
żm1 = xm1ym1 − bzm1

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Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 4270 - 4278
4272
So, the first masked signal c1(t) is given by:
c1(t) = m(t) + xm1(t) (2)
Where: m(t) is the speech signal.
m & s subscript: is the master and slave systems respectively.
In the second block, the Rӧssler chaotic system is used and according to self-synchronization,
by choosing ym2 as the chaotic mask to c1(t) as follows,
Figure 2. Schematic of double masking proposed system
The Rӧssler master model is given by:
ẋm2 = −(ym2 + zm2)
ẏm2 = xm2 + aym2 (3)
żm2 = b + zm2(xm2 − c)
So, the second masked signal c2(t) which sent to the channel is given by:
c2(t) = c1(t) + ym2(t) (4)
At receiver side. The received signal ĉ2(t) is subtracted from the first block (Rӧssler slave system)
by the chaotic signal ys2 to give the recovered signal ĉ1(t) as follows. The Rӧssler slave system is given by:
ẋs2 = −(ys2 + zs2)
ẏs2 = xs2 + ays2 (5)
żs2 = b + zs2(xs2 − c)
So, ĉ1(t) is expressed by:
ĉ1(t) = c2(t) − ys2
ĉ1(t) = (c1(t) + ym2) − ys2 (6)
= c1(t) + er2
where: er2 = ym2 − ys2
Finally the recovered information m̂(t)is obtained by subtracting ĉ1(t) from the chaotic signal xs1of
the second block (Lorenz slave system) as follows. The Lorenz slave system is given by:
ẋs1 = σ(ys1 − xs1)
ẏs1 = rxs1 − ys1 − xs1zs1 (7)
żs1 = xs1ys1 − bzs1

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So, m̂(t) is given by:
m̂(t) = ĉ1(t) − xs1
= (c1(t) + er2) − xs1 (8)
= m(t) + el1 + er2
where: el1 = xm1 − xs1
The synchronization error between Lorenz drive and response systems is given by:
el1 = xm1 − xs1
el2 = ym1 − ys1 (9)
el3 = zm1 − zs1
The synchronization error between Rӧssler drive and response systems is given by:
er1 = xm2 − xs2
er2 = ym2 − ys2 (10)
er3 = zm2 − zs2
2.2. Double masking secure communication system under the AWGN channel
For practical applications the proposed system was tested and simulated with AWGN channel,
so as to study the effect of noise in the reconstructed information and to estimate the amount of power must
be added to the signal in order to receive it at good quality. Figure 3 illustrates the proposed double masking
secure communication system under the AWGN channel.
Figure 3. Double masking proposed system under the AWGN channel
The recovered speech signal is affected by noise as follow:
𝑠(𝑡) = 𝑐2(𝑡) + 𝑛(𝑡) (11)
where 𝒏 (𝒕) is Additive White Gaussian Noise (AWGN).
And referred to (7) then
𝑚̂(𝑡) = 𝑚(𝑡) + 𝑒𝑙1 + 𝑒 𝑟2 + 𝑛(𝑡) (12)
2.3. Noise reduction by digital processing method (DPM)
In this method, the first masked signal c1(t) is transformed from the analog to the digital utilizing
(ADC) converter, before make masking with the chaos signal of the second block of the proposed system.
At the receiver side, the first recovered signal ĉ1(t) which is binary data form will be changed over back to
the analog form by using (DAC) transformer. This conversion of first masked signal to binary form will
reduce the impact of noise on the signal. In fact, the samples that converted from the analog to the binary can
be acted by (1, 2, 3 or 4.... Nb) bits for each sample [18, 19]. Clear speech is obtained, by increase
the number of bits that used. The proposed method is depicted by the subsequent points:

 ISSN: 2088-8708
4274
- Make converting to the samples of the first masked signal 𝑐1(𝑡) from analog to the digital form
using (ADC).
- Masking the changed over data with the second block chaotic signal.
- Add AWGN channel to the masked signal.
- On the receiver side, and after synchronization of the first block slave system, the binary data was
recovered, and then by using (DAC) the sampled data was obtained.
3. QUALITY MEASURMENT OF SPEECH
A numeral of quantities, measurements can be utilized to evaluate the effectiveness of the proposed
system concerning security of encrypted signal and the properties of the recovered speech signal. These are
signal-to noisy ratio (SNR), (LPC) linear predictive code measure, cepstral distance measure (CD) and mean
square error (MSE) [20, 21]. These measurements are defined as follows:
3.1. Segmental spectral signal-to noise ratio
Segmental spectral signal-to noise ratio (SSSNR) is a quantification of noise in a specific signal.
It is a collective measurement of the residual clarity of the encrypted speech and the fineness of
the reconstructed speech. It is specified by the subsequent equation [22]:
SSSNR(s(i), sn(i)) = 10log (
∑ s(i)2i=l
i=1
∑ (s(i)−sn(i))
2i=l
i=1
) (13)
where l is “number of samples”, s(i) is original signal amplitude and sn(i) is “the amplitude” of the encrypted
or decrypted signal.
3.2. Linear pre-dicative code (LPC)
dlpc = ln (
AVAT
BVBT) (14)
where: V is the auto-correlation matrix of the actual speech block, vectors A & B contain the LPC coefficient
for the pure speech block and the recovered or encrypted speech blocks [23].
3.3. Cpestral distance measure (CD)
CD = 10log10 [2 ∑ {Cx(n) − Cy(n)}
2p
n=1 ]
1
2
(15)
where: cy(n) and cx(n) are the cepestral coefficients of the original speech and the reconstructed or
encrypted speech.
3.4. Mean square error (MSE)
MSE =
∑(y−x)2
n
(16)
Is the measure of the recovered speech quality, where the smallest value of MSE means good quality
of the reconstructed speech signal where (x) is “the original signal”, (y) is “the reconstructed signal” and (n)
is “the length vector of the signal” [24, 25].
4. SIMULATION RESULTS
In this part, the theoretical analyses and results are presented to explain the effectiveness of
the proposed system. The speech file entered through the cool edit96 device, the file with 8 kHz, 8bits per
samples, one channel, 42960 samples for 5.3700 second. The simulation results will be presented as follows;
double masking implementation of the speech signal from source to destination, the impact of AWGN
channel noise on speech data at receiver side includes the results of noise reduction by using digital
processing method (DPM), the key space and performance of the proposed model as compared with some
encryption systems are presented.

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4.1. Simulation results of double chaotic masking
Respect to the process of encryption as explained in section (II.A), the original speech signal entered
with 42960 samples, 5.3700 second duration shown in Figure 4(a). Then the masked signal from the first
stage of encryption is given in Figure 4(b). Finally the encrypted signal that produced from the two stages of
master system is given by Figure 4(c).
(a) (b)
(c)
Figure 4. (a) Original speech signal, (b) Masked signal after the first stage of encryption,
(c) The encrypted speech
From the above encrypted signal, it is clear that the entire speech data is hidden inside the chaotic
signal and this is one of the main requirements to obtain the efficient security. On the slave side,
after applying the process of the first and the second de-masking blocks the final recovered speech m̂(t) was
obtained which is nearly exact to the original speech data with mean square error (MSE) = 5.2692 ∗ 10−6
.
4.2. Proposed system under AWGN channel
Double mask secure communication system is subjected and tested under the AWGN channel,
Table 1 illustrates the effect of noise on recovered speech signal for different SNRs. It is noticeable that
the system has poor performance at 32 and 35 dB, while when the SNR is reaching over 37 dB the recovered
speech signal becomes clearer (SSSNR have positive value). To clarify this, different SNRs are simulated as
in Figure 5.
By using the proposed method DPM, the first masked signal was converted from the analog to
the digital form so that the impunity of the change in the masked signal values due to the noise would
increase. Table 2 shows the results that obtained by using the proposed scheme at different SNRs. It is seen
from this table, in case of 15 and 16 dB, the system has poor execution utilizing the three speech
understandability models, from 18 dB and go up, the system execution is enhanced (i.e SSSNR begin to have
positive esteem).

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4276
(a) (b)
Figure 5. The recovered speech at: (a) SNR=35 dB (b) SNR=39 dB
Table 1. Test the quality of recovered speech for
different SNRs
SNR [dB] LPC SSSNR [dB] MSE
29 1.2982 -7.6298 0.0388
32 1.2429 -4.4181 0.0194
35 1.1647 -1.1455 0.0097
37 1.1081 1.0776 0.0061
39 1.0194 3.1674 0.0039
40 0.9980 4.3625 0.0030
Table 2. Test the quality of recovered speech for
different SNRs by using (DPM)
SNR [dB] LPC SSSNR [dB] MSE
15 3.4908 -24.5542 2.5657
16 3.1865 -13.0129 0.8723
18 2.5364 19.0608 0.0080
20 1.0246 31.6376 0.0025
21 0.2531 34.2952 3.7135∗ 10−6
22 0.0993 34.2931 6.0041∗ 10−8
4.3. Key area
Key area is the overall number of “keys” that can be utilized for the encryption strategy. It should be
great enough, to fight the brute force attacker. In our proposed system a high security can be obtained from
the secret key is divided on two system Lorenz and Rӧssler systems as follows:
a) For Lorenz system
The secret key is (σ , r , bl) a floating point precision of ∆= 10−2
is utilized for the secret keys,
therefore the key area is (102)3
= 106
.
b) For Rӧssler system
The secret key is (a, br, c) a floating point precision of ∆= 10−3
is utilized for the secret keys,
therefore the key area is (103)3
= 109
. Therefore, the total key space is (106
∗ 109
) which is a huge key
size to resist the common attack.
4.4. Performance comparison of the some encryption systems and double chaotic masking system
For high security the residual intelligibility of encrypted signal should be small as possible to
prevent any attacker from reach to encrypted data. For more explained about methods that used for
encrypting the self-speech clip considered in this research see [26]. The studied works used are: “Frequency
domain scrambling”, “Time domain scrambling” also “Two dimensional scrambling” respectively as in
Table 3.
Table 3. Proposed model results as compared with some encryption systems
Classical Scrambling LPC SSSNR [dB] CD
Time domain scrambling 0.6532 0.9754 2.4273
Frequency domain scrambling 0.5823 -0.2735 2.5095
Two dimensions scrambling 0.6132 -1.9543 3.2369
Proposed system 0.9751 -21.5620 3.9661
It is obvious from Table 3 that the studied model is much better than other encryption methods.
In public, and after looking to the gained results we observe that the accumulated execution by using doubly
chaotic masking utilized, in terms of “SSSNR” is much reduced (from 0.9754 to -21.5620) compared with
the time domain method and this indicate of decrease the residual intelligibility of the encrypted data.

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5. CONCLUSION
In this paper, an active secure communication system was applied to the information signal based on
the double chaotic masking technique. The following conclusions can be drawn from this study:
(a) The suggested security approach was implemented by using two different stages of chaotic masking on
the signal; one masking was conducted by using Lorenz system and the other masking was built by using
Rӧssler chaotic flow system; (b) A large key space of (106
∗ 109
) was obtained by using this proposed
methodology. The large key space of this model makes the system with a high randomness and more
immunity against attacks; (c) By using this double chaotic masking technique, the residual intelligibility of
the encrypted data decreased where the SSSNR is much reduced from 0.9754 to -21.5620 which is much
lower as compared with some of the previous studies; (d) For usage applications, the proposed system was
tested over an AWGN channel and the results showed that the quality of the recovered information begins to
be clear at a minimum SNR value of 37 dB with an MSE equal to 0.0061; (e) The digital processing method
(DPM) has been combined with this approach to reduce the effect of noise on the recovered information.
This combination led to make the proposed approach work properly at SNR of 21 dB with a mean square
error (MSE) = 3.7135 ∗ 10−6
. Although this combination is very beneficial to improve the quality of
the recovered signal, the complication of the system was increased and this will require some increments in
the bandwidth of the channel due to the change in the data type from the analog to the binary form.
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BIOGRAPHIES OF AUTHORS
Ehab AbdulRazzaq Hussein, PhD. MSc. Electrical Engineering was born in Babylon on
January 1, 1976. He obtained his BSc degree (1997) in Electrical Engineering at the Faculty of
Engineering, University of Babylon and MSc degree (2000), in electrical engineering at
the Department of Electrical Engineering, University of Technology and his PhD. Degree from
the Department of Electrical Engineering at the Faculty of Engineering, University of Basrah,
Currently he works as assistant professor at the Electrical Department at the Faculty of
Engineering, University of Babylon. His main interest is signal processing, analysis, information
transition, sensors and control system analysis.
Murtadha Khafief Khashan, Electrical Engineer was born in ThiQar, 1991. He obtained his
BCs degree (2014) in Electrical Engineering at the Faculty of Engineering, University of Kufa.
Currently he is studying for a master's degree in Electrical Engineering at the Faculty of
Engineering, University of Babylon. His main interest is communication systems, digital signal
processing, measurement and control system analysis.
Ameer K. Jawad was born in Hillah, Iraq, in 1988. He received his BSc degree in Electronic
and Communication Engineering from the Electronic and Communication Engineering
Department, Al-Hossain University College, Karbala, Iraq, in 2012. He obtained his MSc in
Electronic and Communication Engineering from the Electrical Engineering Department,
Al-Mostansiria University, Iraq, in 2015. Since 2015, he has been with the staff of
the Department of Computer Engineering, Islamic University College, Najaf –Iraq.

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