Speech signal encryption decryption using noise signal and

Vol-4 Issues 05, May -2020 ISSN: 2456-9348
Impact Factor: 4.520
International Journal of Engineering Technology Research & Management
IJETRM (http://ijetrm.com/) [49]
SPEECH SIGNAL ENCRYPTION-DECRYPTION USING NOISE SIGNAL AND
PMT
Prof. Yousif Eltous
Dr. Akram Moustafa Hamarchi
Dr. Mohammad S. Khrisat
Dr. Saleh A. Khawatreh
Prof. Ziad Alqadi
Albalqa Applied University, Al Al-Bayt University, Al-Ahliyya Amman University
yousifeltous@yahoo.com
jamarchi@yahoo.com
mkhrisat@bau.edu.jo
skhawatreh@ammanu.edu.jo
natalia_maw@yahoo.com
ABSTRACT
Digital signals, and those including the audio signal, are among the most important types of digital data circulating in
the Internet and through various social media.
The digital audio signal may contain confidential information, or the signal may be of a personal nature, which
requires preventing its understanding from unauthorized entities or persons and therefore this signal must be
encrypted.
In this paper, we will discuss a new method for encrypting the audio signal based on redistributing the signal range
values and on adding a specific noise signal.
The method will be run to measure its efficiency and the extent to which it achieves the level of security and
protection. We will compare this method with other methods used to indicate the preference of the proposed method.
Keywords:
Speech, PMT, MSE, PSNR, encryption time, decryption time, throughput, speedup, noise.
INTRODUCTION
Digital signals [1], [2], [3], among them the sound [4], [5] and color digital picture[17], [18], are among the
most important types of data circulating between people and different institutions because of these types of
importance and wide use in many important and vital applications [6], [7], [8].
The digital signal may carry confidential information [22], [23], [24] or it may be of a personal nature [9], [10].
In these cases, it is necessary to prevent people and unauthorized parties from understanding this signal[33],
[34] or deciphering it to know the content of the digital signal [35], [36], 37], which requires us to provide a safe
way to protect this signal [25], [26], [38].
The audio signal is an analogue signal that is converted into a digital signal through the use of an analogue to
digital converter[19]m [20], [21], which applies as shown in figure 1 sampling and quantization operations to
get the speech signal samples values [11], [12], [13].

Figure 1: Converting speech analogue signal to digital
Based on the foregoing, the digital audio signal can be dealt with as a single-column array (mono speech) or an
array of two columns (stereo speech) consisting of a set of values that indicate the signal range (amplitude) in
different time periods [13], [14], and the number of these values (samples) is determined by the frequency ratio
(sampling frequency) used in the process of conversion [15], [16].
Signal encryption-decryption methods
Signal encrypting means obtaining a destructive and incomprehensible signal from the original signal [25[, [26],
[27]. As for decrypting, it means obtaining a signal that is completely identical to the original signal and without
losing any information from it [28], [29]. Many existing method of cryptography are based on using a private
key and some arithmetic and logic operations to implement encryption-decryption as shown in figure 2 [30],
[31], [32].
Figure 2: Encryption-decryption process
When using a specific method, the following must be taken into consideration [33], [34], [35]:
- The peak-to-signal ratio between the original signal (PSNR) and the encrypted one must be very low
(Mean square error (MSE)) must very high.
- PSNR value between the original signal and the decrypted one must be very high (MSE must be closed
to zero).
- Encryption, decryption time must be very small, making the process to increase the throughput
(number of samples manipulated in a unit of time (second)) [36], [37], [38].
- The method must provide a high level of security and protection by making the process of hacking
impossible or even very difficult [39], [40].
Many methods were introduced to encrypt-decrypt color image, some of these methods were based on image
blocking and XORING the created blocks by a private key [31], [32], [33], [35], [41], [45]. In [36], a method
based on matrix multiplication of the original image and a special generated private key matrix [30]. In [41] the

authors used matrix reordering principle, while in [43] the encryption was based on based on 3D Chaotic Cat
Maps. In [44] the authors introduced a method based on Rubik's Cube principle; these methods will be
implemented to make comparisons with the proposed here method.
The proposed method
The proposed method of speech signal encryption-decryption as shown in figure 3 is based on the following:
Figure 3: Proposed method block diagram
1) A noise signal of huge number of samples is to be generated, this signal must be saved and it will be
used as a private key.
2) From the noise signal we have to extract a segment with size equal the speech signal size, this segment
must be added to the original signal.
3) A partition map table (PMT) must be created, this table is to be used as another private key, and it is
used to divide the signal into partitions, then the partitions must reordered to get the encrypted signal,
table 1 shows an example of used PMT in our experiments:
Table 1: PMT example
Partition number Location Size Order after rearrangement
1 1 2000 3
2 2001 5500 5
3 7501 27500 2
4 35001 60000 4
5 95001 Variable depending on
speech size
1
Figure 4 shows how add/subtract a noise signal to/from a simple signal, while figure 5 illustrates the effect of
using PMT.

-10 0 10
-1
-0.5
0
0.5
1
Original signal
-10 0 10
-2
-1.5
-1
Noise signal
-10 0 10
-3
-2
-1
0
Adding noise
-10 0 10
-1
-0.5
0
0.5
1
Subtracting noise
Figure 4: Adding/subtracting noise signal
-10 -5 0 5 10
-1
0
1
Original signal
-10 -5 0 5 10
-1
0
1
Rearranged signal
-10 -5 0 5 10
-1
0
1
Encrypted Signal
-10 -5 0 5 10
-1
0
1
Decrypted Signal
Figure 5: Rearranging the signals using PMT
The proposed method requires an initialization step, in this step we have to generate a noise signal of huge size
to cover any treated speech signal, this noise signal is to be saved and kwon for the sender and receiver.
The encryption phase can be implemented applying the following steps:
a) Get the speech signal.
b) Retrieve the signal size.
c) Reshape the signal into one raw matrix.
d) Load the noise signal.
e) Extract a segment from the noise signal with size equal to the speech signal size.
f) Add the extracted noise to the speech signal.
g) Select the partitions (location and size of each partition) by creating PMT.
h) Reorder the partitions and update PMT
i) Save PMT
j) Combine the encrypted signal and reshape it to the original.
The decryption phase can be implemented applying the following steps:
a) Get the encrypted speech signal.
b) Retrieve the signal size.
c) Reshape the signal into one raw matrix.
d) Get PMT

e) Reorder the signal according to PMT.
f) Load the noise signal.
g) Extract a segment from the noise signal with size equal to the speech signal size.
h) Subtract the extracted noise to the speech signal.
i) Combine the decrypted signal and reshape it to the original.
Implementation and experimental results
The proposed method was implemented using various speech signals, figure 6 shows a sample output of one
implementation, while figure 7 shows selected set values of each speeches.
0 2 4
x 10
5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Amplitude
Original
0 2 4
x 10
5
-1.5
-1
-0.5
0
0.5
1
1.5
Time (Seconds)
Encrypted
0 2 4
x 10
5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Decrypted
Figure 6: Implementation sample outputs
Figure 7: Outputs sample values
The proposed method was implemented using the speech files recorded with sampling frequency equal to 48000
samples per second; these speeches represent different spoken sentences, table 2 shows the used in the implementation
process speech signals.
Table 2: The used speech signals
Speech number Spoken sentence Size (samples)
1 My name is ziad alqadi and I am from Jordan 250809

2 Stay home stay safe 136566
3 Amman is the capital city of Jordan 199778
4 Good morning every one 144600
5 Good evening ladies and gentles men 181909
6 Albalq Applied University 160994
7 Faculty of engineering technology 188378
8 Computer engineering department 161539
9 Communication technology department 173328
10 Digital signal processing, human speeches 245301
Average 184320
During the encryption-decryption processes each speech signal was divided into 5 partitions as shown in the PMT in
table 1, the number of partitions can be varied and it is not fixed, but the same partitions must be used in the
encryption and decryption phases.
Tables 3 and 4 show the results of implementation:
Table 3: Encryption phase results
Speech
# ET(seconds)
MSE between original and encrypted
speeches
PSNR between original and encrypted
speeches
Throughput (samples per
second)
1 0.0150 0.3447 52.7906 15646000
2 0.0040 0.3441 52.7982 34142000
3 0.0140 0.3442 52.7961 14270000
4 0.0050 0.3430 52.8118 28920000
5 0.0130 0.3431 52.8110 13993000
6 0.0110 0.3458 52.7760 14636000
7 0.0130 0.3419 52.8254 14491000

8 0.0060 0.3422 52.8216 26923000
9 0.0120 0.3415 52.8310 14444000
10 0.0170 0.3442 52.7963 14429000
Average 0.0110 0.3435 52.8058 19189400
Table 4: Decryption phase results
Speech
#
ET(seconds) MSE between original and encrypted
speeches
PSNR between original and encrypted
speeches
Throughput (samples per
second)
1 0.0150 6.6754e-035 389.9200 15646000
2 0.0040 5.0453e-035 391.1359 34142000
3 0.0140 5.6269e-035 390.6621 14270000
4 0.0050 5.4640e-035 390.7897 28920000
5 0.0130 5.7121e-035 390.5969 13993000
6 0.0110 5.9182e-035 390.4429 14636000
7 0.0130 4.9074e-035 391.2563 14491000
8 0.0060 5.2802e-035 390.9383 26923000
9 0.0120 4.4161e-035 391.7144 14444000
10 0.0170 5.6580e-035 390.6382 14429000
0.0110 5.3365e-035 390.8095 19189400
From the obtained experimental results shown in tables 3 and 4 we can see the following:
- PSNR value between the original speech signal and the encrypted one is low (High MSE), which means that
the encryption process destroys the original signal and made it destructive and incomprehensible signal.
- PSNR value between the original speech signal and the decrypted one is high (low MSE), which means that
the decryption process generates a signal identical to the original.
- The encryption decryption times are very low, thus the provide method provides a high throughput.

- The provide method provide a high security and protection level by using two keys the noise signal and PMT
and it is difficult to know or guess them.
- The propose method has some advantages comparing with other methods results by increasing the process
throughput, and it has a speedup always greater than one as shown in table 5
Table 5: Results comparisons
Method Encryption time (s) Decryption time (s) Speedup of the proposed
method
Order
Proposed SSEAM 0.0110 0.0110 `1 1
Ref. [29] 0.0513 0.0513 4.6636 2
Ref. [39] 0.06469 0.062727 5.8809 3
Ref. [40] 0.23 0.23 20.9091 5
Ref. [41] 0.5 0.5 45.4545 7
Ref. [42] 0.4 0.4 36.3636 6
Ref. [43] 0.12 0.12 10.9091 4
Ref. [44] v.1 0.56 0.56 50.9091 8
Ref [45] v.2 1.01 1.01 91.8182 9
Conclusion
A simple method of speech signal encryption-decryption was proposed, this method is highly secure and protected by
using two keys; the noise signal and PMT. The proposed method was implemented using various speech signals, and
the obtained experimental results showed that the proposed is very efficient and it satisfies the requirement of
encryption-decryption process.
References
[1] Matrouk, A Al-Hasanat, H Alasha'ary, Z Al-Qadi, H Al-Shalabi, Speech fingerprint to identify isolated word person, World
Applied Sciences Journal, vol. 31, issue 10, pp. 1767-1771, 2014.
[2] Jamil Azzeh, Bilal Zahran, Ziad Alqadi, Salt and Pepper Noise: Effects and Removal, International Journal on
Informatics Visualization, vol. 2, issue 4, pp. 252-256, 2018.
[3] Ayman Al-Rawashdeh, Ziad Al-Qadi, Using wave equation to extract digital signal features, Engineering,
Technology & Applied Science Research, vol. 8, issue 4, pp. 1356-1359, 2018.
[4] ] Ziad AA Alqadi, Investigation and analysis of mat lab models used to solve second order linear ODE,
International Journal on Numerical and Analytical Methods in Engineering, vol. 2, issue 1, 2014.
[5] Ziad A AlQadi Amjad Y Hindi, O Dwairi Majed, PROCEDURES FOR SPEECH RECOGNITION USING LPC
AND ANN, International Journal of Engineering Technology Research & Management, vol. 4, issue 2, pp. 48-55,
2020.
[6] Ziad Alqad, Prof. Yousf Eltous Dr. Ghazi M. Qaryouti, Prof. Mohammad Abuzalata, Analysis of Digital Signal
Features Extraction Based on LBP Operator, International Journal of Advanced Research in Computer and
Communication Engineering, vol. 9, issue 1, pp. 1-7, 2020.
[7] Amjad Y. Hindi, Majed O. Dwairi, Ziad A. AlQadi, Creating Human Speech Identifier using WPT, International
Journal of Computer Science and Mobile Computing, vol. 9, issue 2, pp. 117 – 123, 2020.
[8] Ismail Shayeb, Ziad Alqadi, Jihad Nader, Analysis of digital voice features extraction methods, International
Journal of Educational Research and Development, vol. 1, issue 4, pp. 49-55, 2019.
[9] Aws Al-Qaisi, Saleh A Khawatreh, Ahmad A Sharadqah, Ziad A Alqadi, Wave File Features Extraction Using
Reduced LBP, International Journal of Electrical and Computer Engineering, vol. 8, issue 5, pp. 2780, 2018.
[10] Prof. Yousif Eltous Dr. Amjad Hindi, Prof. Ziad Alqadi, Dr. Ghazi M. Qaryouti, Prof. Mohammad Abuzalata,
Using FIR Coefficients to Form a Voiceprint, International Journal of Innovative Research in Electrical, Electronics,
Instrumentation and Control Engineering, vol. 8, issue 1, pp. 1-6, 2020.

[11] Dr. Ghazi M. Qaryouti, Prof. Mohammad Abuzalata, Prof. Yousf Eltous, Prof. Ziad Alqadi, Comparative Study
of Voice Signal Features Extraction Methods, IOSR Journal of Computer Engineering (IOSR-JCE), vol. 22, issue 1,
pp. 58-66, 2020.
[12] Amjad Y. Hindi, Majed O. Dwairi, Ziad A. AlQadi, Analysis of Digital Signals using Wavelet Packet Tree,
IJCSMC, vol. 9, issue 2, pp. 96-103, 2020.
[13] Yousf Eltous Ziad A. AlQadi, Ghazi M. Qaryouti, Mohammad Abuzalata, ANALYSIS OF DIGITAL SIGNAL
FEATURES EXTRACTION BASED ON KMEANS CLUSTERING, International Journal of Engineering
Technology Research & Management, vol. 4, issue 1, pp. 66-75, 2020.
[14] Prof. Yousif Eltous, Dr. Ghazi M. Qaryouti, Prof. Mohammad Abuzalata, Prof. Ziad Alqadi, Evaluation of Fuzzy
and C_mean Clustering Methods used to Generate Voiceprint, IJCSMC, vol. 9, issue 1, pp. 75 -83, 2020.
[15] Majed O. Al-Dwairi, Amjad Y. Hendi, Mohamed S. Soliman, Ziad A.A. Alqadi, A new method for voice signal
features creation, International Journal of Electrical and Computer Engineering (IJECE), vol. 9, issue 5, pp. 4092-
4098, 2019.
[16] Dr. Amjad Hindi Dr. Majed Omar Dwairi Prof. Ziad Alqadi, PROCEDURES FOR SPEECH RECOGNITION
USING LPC AND ANN, International Journal of Engineering Technology Research & Management, vol. 4, issue 2,
pp. 48-55, 2020.
[17] Haitham Alasha'ary, Abdullah Al-Hasanat, Khaled Matrouk, Ziad Al-Qadi, Hasan Al-Shalabi, A Novel Digital
Filter for Enhancing Dark Gray Images, European Journal of Scientific Research , pp. 99-106, 2014.
[18] Majed O Al-Dwairi, Ziad A Alqadi, Amjad A Abujazar, Rushdi Abu Zneit, Optimized true-color image
processing, World Applied Sciences Journal, vol. 8, issue 10, pp. 1175-1182, 2010.
[19] Jamil Al Azzeh, Hussein Alhatamleh, Ziad A Alqadi, Mohammad Khalil Abuzalata, Creating a Color Map to be
used to Convert a Gray Image to Color Image, International Journal of Computer Applications, vol. 153, issue 2, pp.
31-34, 2016.
[20] Musbah J. Aqel, Ziad A. Alqadi, Ibraheim M. El Emary, Analysis of Stream Cipher Security Algorithm, Journal
of Information and Computing Science, vol. 2, issue 4, pp. 288-298, 2007.
[21] Jamil Al-Azzeh, Ziad Alqadi, Mohammed Abuzalata, Performance Analysis of Artificial Neural Networks used
for Color Image Recognition and Retrieving, International Journal of Computer Science and Mobile Computing, vol.
8, issue 2, pp. 20 – 33, 2019.
[22] Amjad Y Hindi, Majed O Dwairi, Ziad A AlQadi, A Novel Technique for Data Steganography, Engineering,
Technology & Applied Science Research, vol. 9, issue 6, pp. 4942-4945, 2019.
[23] Mutaz Rasmi Abu Sara Rashad J. Rasras, Ziad A. AlQadi, A Methodology Based on Steganography and
Cryptography to Protect Highly Secure Messages, Engineering, Technology & Applied Science Research, vol. 9,
issue 1, pp. 3681-3684, 2019.
[24] Dr. Amjad Hindi, Dr. Ghazi M. Qaryouti, Prof. Yousif Eltous, Prof. Mohammad Abuzalata, Prof. Ziad Alqadi,
Color Image Compression using Linear Prediction Coding, International Journal of Computer Science and Mobile
Computing, vol. 9, issue 2, pp. 13 – 20, 2020.
[25] Prof. Ziad Alqadi, Dr. Mohammad S. Khrisat, Dr. Amjad Hindi, Dr. Majed Omar Dwairi, USING SPEECH
SIGNAL HISTOGRAM TO CREATE SIGNAL FEATURES, International Journal of Engineering Technology
Research & Management, vol. 4, issue 3, pp. 144-153, 2020.
[26] Ziad A. AlQadi, A Highly Secure and Accurate Method for RGB Image Encryption, IJCSMC, vol. 9, issue 2, pp.
12-21, 2020.

[27] Belal Zahran Rashad J. Rasras, Ziad Alqadi, Mutaz Rasmi Abu Sara, Developing new Multilevel security
algorithm for data encryption-decryption (MLS_ED), International Journal of Advanced Trends in Computer Science
and Engineering, vol. 8, issue 6, pp. 3228-3235, 2020.
[28] Ziad Alqad, Majid Oraiqat, Hisham Almujafet, Salah Al-Saleh, Hind Al Husban, Soubhi Al-Rimawi, A New
Approach for Data Cryptography, International Journal of Computer Science and Mobile Computing, vol. 8, issue 9,
pp. 30-48, 2019.
[29] Majed O Al-Dwairi, A Hendi, Z AlQadi, An efficient and highly secure technique to encrypt-decrypt color
images, Engineering, Technology & Applied Science Research, vol. 9, issue 3, pp. 4165-4168, 2019.
[30] Amjad Y Hendi, Majed O Dwairi, Ziad A Al-Qadi, Mohamed S Soliman, A novel simple and highly secure
method for data encryption-decryption, International Journal of Communication Networks and Information Security,
vol. 11, issue 1, pp. 232-238, 2019.
[31] Ziad Alqadi, Ahmad Sharadqh, Naseem Asad, Ismail Shayeb, Jamil Al-Azzeh, Belal Ayyoub, A highly secure
method of secret message encoding, International Journal of Research in Advanced Engineering and Technology, vol.
5, issue 3, pp. 82-87, 2019.
[32] Prof. Ziad Alqadi, Dr. Mohammad S. Khrisat, Dr. Amjad Hindi, Dr. Majed Omar Dwairi, Simple and Highly
Secure, Efficient and Accurate Method (SSEAM) to Encrypt-Decrypt Color Image, International Journal of
Advanced Research in Computer and Communication Engineering, vol. 9, issue 4, pp. 64-69, 2020.
[33] Amjad Y Hendi, Majed O Dwairi, Ziad A Al-Qadi, Mohamed S Soliman, A novel simple and highly secure
method for data encryption-decryption, International Journal of Communication Networks and Information Security,
vol. 11, issue 1, pp. 232-238, 2019.
[34] Ziad Alqadi, Ahmad Sharadqh, Naseem Asad, Ismail Shayeb, Jamil Al-Azzeh, Belal Ayyoub, A highly secure
method of secret message encoding, International Journal of Research in Advanced Engineering and Technology, vol.
5, issue 3, pp. 82-87, 2019.
[35] Rushdi Abu Zneit, Jamil Al-Azzeh, Ziad Alqadi, Belal Ayyoub, Ahmad Sharadqh, Using Color Image as a
Stego-Media to Hide Short Secret Messages, IJCSMC, Vol. 8, Issue 6, pp. 106 –123, 2019.
[36] Qazem Jaber Rashad J. Rasras, Mohammed Abuzalata, Ziad Alqadi, Jamil Al-Azzeh, Comparative Analysis of
Color Image Encryption-Decryption Methods Based on Matrix Manipulation, IJCSMC, vol. 8, issue 3, pp. 14-26,
2019.
[37] Jamil Al-Azzeh, Bilal Zahran, Ziad Alqadi, Belal Ayyoub, Muhammed Mesleh, A Novel Based On Image
Blocking Method To Encrypt-Decrypt Color, International Journal on Informatics Visualization, vol. 3, issue 1, pp.
86-93, 2019.
[38] Jamil Al-Azzeh, Ziad Alqadi, Qazem Jaber, A Simple, Accurate and Highly Secure Method to Encrypt-Decrypt
Digital Images, INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, VOL 3 (2019) NO 3, pp.
262-265.
[39] S. Wang, Y. Zheng, Z. Gao, “A New Image Scrambling Method through Folding Transform”, IEEE International
Conference on Computer Application and System Modeling, Taiyuan, China, October 22-24, 2010.
[40] G. Ye, “An Efficient Image Encryption Scheme based on Logistic maps”, International Journal of Pure and
Applied Mathematics, Vol. 55, No. 1, pp. 37-47, 2009.
[41] T. Sivakumar, R. Venkatesan, “A Novel Image Encryption Approach using Matrix Reordering”, WSEAS
Transactions on Computers, Vol. 12, No. 11, pp. 407-418, 2013.

[42] H. Gao, Y. Zhang, S. Liang, D. Li, “A New Logistic maps for Image Encryption”, Chaos- Solutions & Fractals,
Vol. 29, No. 2, pp. 393- 399, 2006.
[43] G. Chen, Y. Mao, C. K. Chui, “A Symmetric Image Encryption Scheme based on 3D Chaotic Cat Maps”, Chaos,
Solutions & Fractals, Vol. 21, No. 3, pp. 749–761, 2004.
[44] K. Loukhaoukha, J. Y. Chouinard, A. Berdai, “A Secure Image Encryption Algorithm Based on Rubik's Cube
Principle”, Journal of Electrical and Computer Engineering, Vol. 2012, Article ID 173931, pp. pp. 1-13, 2012.
[45] X. Wang, J. Zhang, “An Image Scrambling Encryption using Chaos-controlled Poker Shuffle Operation”, IEEE
International Symposium on Biometrics and Security Technologies, Islamabad, Pakistan, April 23-24, 2008.

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