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Title

Study on scramble method for speech signal byusing random‑bit shift of quantization

Author(s)Zhu, Zhi; Yamamoto, Katsuhiko; Unoki, Masashi;
Aoki, Naofumi

Citation

Journal of Signal Processing, 18(6): 303‑307

Issue Date

2014

Type

Journal Article

Text version

publisher

URL

http://hdl.handle.net/10119/12890

RightsCopyright (C) 2014 信号処理学会. Zhi Zhu,
Katsuhiko Yamamoto, Masashi Unoki, and Naofumi
Aoki, Journal of Signal Processing, 18(6), 2014,
303‑307. http://dx.doi.org/10.2299/jsp.18.303

Description

Japan Advanced Institute of Science and Technology

Journal of Signal Processing, Vol.18, No.6, pp.303-307, November 2014

RESEARCH NOTE

Study on Scramble Method for Speech Signal
by Using Random-Bit Shift of Quantization
Zhi Zhu1, Katsuhiko Yamamoto1, Masashi Unoki1 and Naofumi Aoki2
1

School of Information Science, Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
2
Graduate School of Information Science and Technology, Hokkaido University
Kita-14 Nishi-9, Kita-ku, Sapporo 060-0814, Japan
E-mail: {kyamamoto, zhuzhi, unoki}@jaist.ac.jp, aoki@ime.ist.hokudai.ac.jp

Abstract
Speech scrambling aims to eliminate intelligibility of original speech in order to preventing
eavesdropping and copyright infringement. There is, however, a problem in that completely recovering
scrambled speech into the original speech cannot be achieved with conventional methods. In this paper, we
propose a speech scrambling method that uses the random-bit-shift of quantization bits with common keys. We
evaluated the confidentiality and efficiency of the proposed method by using two objective measures, SER andPESQ. As a result we confirmed that speech signals can be scrambled into completely unintelligible sounds withthe proposed method. Moreover, it is possible to restore a scrambled speech signal into the original one
completely. In addition, we also confirmed that the scrambled speech signal could not be descrambled correctlywith the wrong key.
Keywords: speech scramble, random bit shift, PCM speech signal, secure voice

1.

Introduction

Many other speech-scrambling methods for speech coding,such as scrambler using variation in bit-reversal sensitivity [4] and one that focuses on the AMR codec [5], were
proposed. They are effective at speech signal encryption.
However, poor voice quality may result and there is aproblem in that completely recovering a signal seems
unachieved.
In this paper, we propose a speech scrambling methodthat uses the random-bit shift of quantization with acommon key. Since the descrambling process is aninverse operation of the scrambling process. The
scrambled speech signal can be completely recovered into
the original signal with the proposed method.

Information protection and the confidentiality of digitalcontent are becoming more important today. With therapid development of multimedia and Internet technologies, digital speech content is being transmitted over
networks more frequently. Therefore, reliable security forthe storage and transmission of digital speech contents isrequired for many areas, such as for preventing eavesdropping and copyright infringement. The speech scrambling method is a method where the sender encrypts
digital speech information and only the people who thesender allows can decrypt the scrambled speech signal touse it [1]. It is similar to but not a direct application of
normal cryptographic techniques. The method is very
2. Proposed Method
important for preventing eavesdropping and copyright
infringement.
Figure 1 shows an overview of the proposed method.
Two representative speech scrambling methods are The main idea of the method is that a bit sequence of eachreverse scrambling in the frequency domain [2] and sample in a PCM speech signal is subjected to a circulartime-segment permutation in time-domain scrambling [3]. left shift of random bit position in order to completely

Journal of Signal Processing, Vol. 18, No. 6, November 2014

303

Original
signal

PRNG

KEY

PRNG

Random bit
left shift

Scrambled
signalRandom bit
right shift

Descrambled
signalFig. 1 Overview of proposed method(a)1

Amplitude

Random number = 3
3-bit circular left shift to scramble
3-bit circular right shift to descramble

0.5
0
−0.5
−1

0.5

1

0

0.5

1

0

0.5

1

1.5

2

2.5

3

1.5

2

2.5

3

1.5

2

2.5

3

Time (s)1

Amplitude

Circular left bit shift0(b)a0 a1 a2 … a12 a13 a14 a15

0.5
0
−0.5
−1

a13 a14 a15 a0 ... a10 a11 a12(c)Time (s)Circular right bit shiftFig. 2 Scrambling based on circular bit shift
scramble the original speech signal. A common key isused as the seed of pseudorandom number generator to
produce a predictable sequence of numbers. Each number
is the bit position that should be shifted. The common key
could be any nonnegative integer. In addition, in order tobe able to completely recover scrambled speech into theoriginal speech, we carry out circular right shift of random
bit position operation on each sample. The bit position isshifted equal to the value when it was scrambled by taking
advantage of the common key.
2.1 Random number generator

Amplitude

1
0.5
0
−0.5
−1

Time (s)Fig. 3 Example of proposed method: (a) Original speech,(b) Scrambled speech, (c) Descrambled speech
2.2 Random-bit shift
The scrambling and descrambling are done by circular
bit shift operation, as illustrated in Fig. 2. With circular bit
shift, the value of the amplitude for a PCM speech signal
will be changed. There is, for instance, the binary number(0100110)2 = (38)10. With a circular left bit shift of 3-bit
positions, this number will be changed to (0110010)2 =(50)10. In the proposed method, the bit position of
circular bit shift operation is based on the sequence ofrandom numbers generated by the pseudorandom number
generator. The steps for scrambling by using the proposed
method are shown as follows.
(1) A PCM speech signal is expressed as a sequence ofbinary numbers:x(m) = (a0 a1a2 !an−2 an−1 )2(1)

The Mersenne twister is used as a random number
generator [6], which is the default pseudorandom number
generator (PRNG) in Matlab. Mersenne twister is the first
PRNG to provide fast generation of high-quality
pseudorandom integers that have the colossal period of
219937-1 iterations. The function “rng” in Matlab is used towhere m = 0, 1, …, M-1, and M is the number ofcontrol the seed of the PRNG (key for scrambler) so thatsamples of the signal; n is the quantization bit rate.
the random number generator can produce a predictablesequence of numbers. In other words, if the key is (2) Use an integer (common key) as the seed for thepseudorandom number generator to generate aidentical, the sequence of random numbers will always be
sequence of uniform random integers r(i). The
identical. The length of the sequence of random numbers
random numbers should be drawn from the discrete
is equal to the number of samples in a PCM speech signal.
uniform distribution on the interval [0, n-1].
Each random number is the shift amount of the bit shift
for each sample. Each random number is the shift amount (3) For sample m, the circular bit left shift r(m) bit
operation is done on x(m).
of the bit shift for each sample.

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Journal of Signal Processing, Vol. 18, No. 6, November 2014

(a)Amplitude

1
0.5
0
−0.5
−1

0

(b)

0.5

1

1.5

Time (s)

2

2.5

3

Amplitude

1
0.5
0
−0.5
−1

0

0.5

1

0

0.5

1

(c)

1.5

2

2.5

3

1.5

2

2.5

3

Time (s)Amplitude

1
0.5
0
−0.5
−1

Time (s)Fig. 4 Example of proposed method: (a) Original speech,(b) Scrambled speech, (c) Speech descrambled withwrong key(4) Apply step (3) to each sample of the original speech
signal.
The descrambling process involves changing thecircular bit left shift to a right shift in step (3), and theother steps are all the same as in the scrambling process.
This is an inverse operation of the scrambling process. If
the key used to generating random integers in step (2) iscorrect, scrambled speech signal can be descrambled into
the original one completely.
An example of the proposed method is shown in Fig. 3.
An integer “5” is used as the common key here. Figures3(a)-(c) show the original speech signal, scrambled speech
signal, and the speech signal descrambled from scrambled
speech. In Figs. 3(b) and (c), as a result, we can see thatthe original speech was scrambled into an unintelligible
sound signal and the scrambled speech signal wasrecovered into the original speech signal completely.
3.

Evaluations

The confidentiality and efficiency of the proposed
method were evaluated by carrying out two objective
measures, signal-to-error ratio (SER) in dB and perceptual
evaluation of speech quality (PESQ) with an objective
difference grade (ODG). SER shows the error ratio
between the target signal and the original signal and isdefined by the following equation.
# ∑f2 &
1
( [dB]SER = 10 log10 %% ∑ ( f − f )2 (
1
2
'
$

(2)

Journal of Signal Processing, Vol. 18, No. 6, November 2014

SER [dB]
PESQ
[ODG]

Table 1 Results of evaluations(mean and standard deviation)
Descrambled
Scrambled Descrambled
by wrong
signalsignalkey-12.1 (1.31)∞ (0)-12.1 (1.29)
0.62 (0.19)

4.5(0)

0.62 (0.19)

A smaller value of SER means a higher error between thetarget signal and the original signal. When the value is ∞dB, the result indicates that the target signal is the same as
the original signal. PESQ is a standard for objective voice
quality testing. It provides scores in the range –0.5 to 4.5
to map the value of mean opinion score (MOS) from 1(Bad) to 5 (Excellent). 20 stimuli from a databaseproduced by Fujitsu Laboratory were used to evaluate theproposed method. The same sampling frequency of 22.05
kHz and 16-bit resolution were used for all stimuli.
Table 1 shows the results of the evaluation. For thescrambled speech signal, the mean value of SER was
negative, and the mean value of PESQ was close to 0. As
a result, we confirmed that the original speech signal had
been completely scrambled by proposed method. In otherword the original speech signal was converted into similar
white noise, and the linguistic information of the original
speech was broken completely. In addition, for thedescrambled speech signal, the mean value of SER andPESQ were ∞ dB and 4.5 ODG. The results confirmedthat the scrambled speech signal could be completely
recovered into the original speech signal. The results werealso confirmed with preliminary subjective tests.
In addition, a case of where the scrambled speech is
descrambled with the wrong key is shown in Fig. 4(c). For
the scrambled speech, the common key integer “5” wasused as the seed of the random number generator. Figure
4(c) is speech incorrectly descrambled with the wrong key“6”. From the waveform we can see that the scrambled
speech could not be recovered into the original speech
signal. The results of objective evaluation are also shownin Table 1. We can see there was not much change both in
the SER and PESQ. This means that the speech
descrambled with the wrong key is still unintelligible.
4.

Discussion

As an additional consideration, we investigated theconfidentiality of the proposed method. The random-bit
shift is applied to each sample with the proposed method.

305

Amplitude

1
0.5
0
−0.5
−1

Amplitude

1

0

0.05

0.1

0.15

0.2

(b)

0.25

Time (s)

0.3

0.35

0.4

0.45

0.5

0.46 (0.20)

4.5(0)

0
−0.5

1

0

0.05

0.1

0.15

0.2

0.05

0.1

0.15

0.2

(c)

0.25

0.3

0.35

0.4

0.45

0.5

0.25

0.3

0.35

0.4

0.45

0.5

Time (s)

0.5
0
−0.5
−1

0

Time (s)Fig. 5 Example of improved method: (a) Original speech
signal, (b) Scrambled speech signal, (c) Scrambled speech
signal with disrupting the order of samples
However, the order of the samples of the scrambled
speech is the same as the original one. To improve
confidentiality, we try to disrupt the order of samples at
random in order to make descrambling by brute force
attack more difficult. The amount of calculation needed
for an attacker to break the whole scrambled speech signalof n seconds and fs kHz sampling frequency is 16n×fs forthe proposed method. However, by disrupting the order ofsamples, an adversary has to complete (n×fs)!×16n×fs tries
before they can recover the scrambled speech into theoriginal speech. For a scrambled speech signal of 1 s andat a sampling frequency of 20 kHz, this number is
20000!×1620000. This security level appears to be
sufficient.
Figures 5(a)-(c) show an example of the abovementioned method. Figure 5(a) is a speech signal with one
word. Figure 5(b) is the scrambled speech without theorder of samples disrupted and Figure 5(c) is thescrambled speech with the order of samples disrupted. As
a result, we can see that the effect of scrambling on the
non-speech period was less than the speech period in Fig.
5(b). The reason is the value of samples in the non-speech
period was very small; therefore, the effect of circular bit
shift was not that much as well. It is easy to distinguishthe speech period and non-speech period from the
waveform. However, by disrupting the order of samples,the impact of the scrambling increased, and the scrambled
speech was more like Gaussian white noise.
The results of objective evaluation of the improved
method with the same stimuli are shown in Table 2. We

306

Table 2 Results of evaluations on improved method(mean and standard deviation)
Scrambled
Descrambled
signalsignalSER [dB]-12.1 (1.31)∞ (0)PESQ [ODG]

0.5

−1

Amplitude(a)can see that the mean value of SER did not change thatmuch, whereas the mean value of PESQ decreased from
0.62 to 0.46. This means that the voice quality deteriorated further, and the original speech was scrambled moredeeply. We also confirmed that the scrambled speech
signal could still be recovered into the original one.
5.

Conclusion

In this paper, we proposed a speech scrambling methodthat uses the random-bit shift of quantization bits with acommon key. The results of evaluations showed that theoriginal speech signal could be scrambled into an
unintelligible audio signal. We also confirmed that thescrambled speech signal could be recovered into theoriginal signal completely. We also discussed improving
the confidentiality of the proposed method by disrupting
the order of samples. Our future work will be on using the
proposed method for not only PCM speech signals but
also compressed speech signals like MPEG.
Acknowledgments
This work was supported by a Grant-in-Aid for
Scientific Research (B) (No. 23300070) and the Fostering
ICT Global Leader Program.
References[1] N. S. Jayant: Analog scramblers for speech privacy,
Computers & Security, Vol. 1, pp. 275-289, 1982.
[2] L. Lee, G. Chou and C. Chang: A new frequency
domain speech scrambling system which does not
require frame synchronization, IEEE Transactions on
Communications, Vol. 32, No. 4, pp. 444-456, 1984.
[3] N. S. Jayant, B. McDermott, S. Christensen and A.
Quinn: A comparision of four methods for analog
speech privacy, IEEE Transcations on Communications, Vol. COM-29, No. 1, pp. 18-23, 1981.

Journal of Signal Processing, Vol. 18, No. 6, November 2014

[4] A. Kataoka and S. Hayashi: A cryptic encoding
method for G.729 using variation in bit-reversal
sensitivity, IEICE Transactions (Japanese Edition),Vol. J87-D-II, No. 6, pp. 1224-1232, 2004.
[5] J. F. de Andrade, M. L. R. de Campos and J. A.
Apolina ́rio: Speech privacy for modern mobile communication systems, Proc. ICASSP2008, pp.
1777–1780, 2008.
[6] M. Matsumoto and T. Nishimura: Mersenne twister: A
623-dimensionally equidistributed uniform pseudorandom number generator, ACM Transactions on
Modeling and Computer Simulation (TOMACS), Vol.
8, No. 1, pp. 3-30, 1998.
[7] ITU P.862: Perceptual evaluation of speech quality(PESQ): An objective method for end-to-end speech
quality assessment of narrow-band telephone networks
and speech codecs, 2001.

speech individuality.

Zhi Zhureceived his B.E.
degree in communication engineering from Nanjing Universityof Posts and Telecommunications, Nanjing, China, in 2012.
Now he is studying at the Master's course in the Japan Advanced Institute of Science andTechnology (JAIST). He is interested in the secure voice andKatsuhiko Yamamoto
received his B.E. degree from
Kobe City College of Technology in 2013. Now he is studying
at the Master's course in Japan
Advanced Institute of Scienceand Technology (JAIST). He isinterested in auditory perception
and models.

Journal of Signal Processing, Vol. 18, No. 6, November 2014

Masashi Unoki
received his
M.S. and Ph.D. degrees from theJapan Advanced Institute of
Science and Technology in 1996
and 1999, respectively. His main
research interests are in auditory
motivated signal processing andthe modeling of auditory systems.
He was a Japan Society for the
Promotion of Science (JSPS)
research fellow from 1998 to
2001. He was associated with the ATR Human
Information Processing Laboratories as a visiting
researcher from 1999-2000, and he was a visiting research
associate at the Centre for the Neural Basis of Hearing(CNBH) in the Department of Physiology at the
University of Cambridge from 2000 to 2001. He has beenwith the Faculty of School of Information Science at
JAIST since 2001 and is now an Associate Professor. He
is a member of the Research Institute of Signal Processing(RISP), the Institute of Electronics, Information andCommunication Engineers (IEICE) of Japan, and the
Acoustical Society of America (ASA). He is also amember of the Acoustical Society of Japan (ASJ), and theInternational Speech Communication Association (ISCA).
Dr. Unoki received the Sato Prize from the ASJ in 1999
and 2010 for an Outstanding Paper and the Yamashita
Taro “Young Researcher” Prize from the Yamashita Taro
Research Foundation in 2005.

Naofumi Aoki
received his
Ph.D. degree from Hokkaido
University in 2000. He is currently an Assistant Professor at
the Graduate School of Information Science and Technology,
Hokkaido University. He is engaged in the research on media
information processing.

(Received April 24, 2014; revised September 3, 2014)307