Journal of Robotics, Networking and Artificial Life
Vol. 6(2); September (2019), pp. 133–137
DOI: https://doi.org/10.2991/jrnal.k.190828.013; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

An Authentication Method for Digital Audio using Wavelet
Transform and Fundamental Frequencies
Yasunari Yoshitomi1,*, Shohei Tani2, Masaki Arasuna3, Ryota Kan4, Taro Asada1, Masayoshi Tabuse1
Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, 1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, JapanFukuchiyama City Hall, 13-1 Miki, Fukuchiyama, Kyoto Prefecture 620-8501, Japan
3Nissay Information Technology Co., Ltd., 5-37-1 Kamata, Ohta-ku, Tokyo 144-8721, Japan
4Shimazu Business Systems Co., Ltd., 1 Kuwahara-cho Nishinokyo, Nakagyo-ku, Kyoto 604-8442, Japan
12ARTICLE INFO
Article History
Received 18 October 2018
Accepted 05 December 2018
Keywords
Authentication
audio
copyright protection
wavelet transform
fundamental frequency

ABSTRACT
Several digital watermarking techniques for audio files have been proposed for hiding data for protecting their copyrights. Thereis a tradeoff between the quality of watermarked audio and the tolerance of watermarks to signal processing methods, such ascompression. To overcome the inevitable tradeoff, we previously developed an authentication method for digital audio. We have
improved the method by determining the region to be authenticated in the audio data by making effective use of the fundamental
frequency characteristics.

© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Recent progress in digital media technology and distribution systems, such as the Internet and cellular phones, has enabled consumers to easily access, copy, and modify digital audio. Several
Digital Watermarking (DW) techniques for audio files have been
proposed for hiding data for protecting their copyrights. There isgenerally a tradeoff between the quality of watermarked audio andthe tolerance of watermarks to signal processing methods, such ascompression.
In previous research [1], to essentially overcome this issue, wedeveloped an authentication method for digital audio to protect thecopyrights. In contrast to DW, no additional information is inserted
into the original audio by the previously proposed method, andthe digital audio is authenticated using features extracted using a
Discrete Wavelet Transform (DWT) and characteristic coding of
the previously proposed method [1]. However, in the previouslyproposed method [1], it is indispensable to determine the region tobe authenticated in the audio data by using the fixed length and thefixed starting time from the beginning of the audio data. Therefore,
the authentication tolerance to clipping of the audio data is essentially insufficient for practical use.
In the present study, to overcome this issue, we have improved themethod by determining the region to be authenticated in the audio
data by using the fundamental frequency characteristics.
Corresponding author. Email: yoshitomi@kpu.ac.jp*

2. OBSERVED PHENOMENON UNDERPINSTHE AUTHENTICATION METHOD
The procedure and algorithm of our previously proposed method[1] is reviewed in this section, because it is very important for thepresent study.
It has been observed that when a DWT is applied to audio data, in
the histogram of the wavelet coefficients of the Multi-Resolution
Representation (MRR), the center of the distribution is very close
to zero [2]. We exploited this phenomenon to develop an authentication method for audio data [1]. For further information on
the DWT used, see Inoue and Yoshitomi [3] and Taniguchi andYoshitomi [4].

3. AUTHENTICATION RATIO
We set the authentication parameters as described below [1,5].
In Figure 1, Th′(minus) was chosen so that it divides the nonpositive wavelet coefficients ( Sm¢ in total frequency) into two equalgroups, and similarly Th′(plus) was chosen so that it divides thepositive wavelet coefficients ( S ¢p in total frequency) into two equalgroups. Next, the values of the parameters T1′–T4′, which control
the authentication precision, were chosen such that the following
conditions were satisfied:(1) T1′ < Th′(minus) < T2′ < 0 < T3′ < Th′(plus) < T4′.

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To use the proposed method, we need to store the flags fi and the original code C for each copyrighted file that we want to protect. Whencalculating (1) to authenticate audio data, we do not use the originalaudio data; instead, we use the flags fi and the code C for that file [1].

4. FUNDAMENTAL FREQUENCYCHARACTERISTICS OF AUDIO DATA

Figure 1 | Three sets (F, G, and H) of MRR wavelet coefficients used forauthentication [1].

¢ , the number of wavelet coefficients in (T1′,(2) The value of ST1Th′(minus)), is equal to ST¢ 2 , the number of wavelet coefficients in [Th′(minus), T2′), i.e., ST¢ 1 = ST¢ 2 .
(3) The value of ST¢ 3 , the number of wavelet coefficients in (T3′,Th′(plus)], is equal to ST¢ 4 , the number of wavelet coefficientsin (Th′(plus), T4′), i.e., ST¢ 3 = ST¢ 4 .
(4) ST¢ 1 / Sm¢ = ST¢ 3 / S ¢p .
In the present study, the values of both ST¢ 1 / Sm¢ and ST¢ 3 / S ¢p are set
to 0.3, which is the same setting used for creating the code for theoriginal audio data [1]. When preparing the authentication codes,
the wavelet coefficients V′ for each MRR sequence are divided asshown in Figure 1 into three sets, which are defined as follows:

Figure 2 shows the fundamental frequency of the first entry of therock music genre category in the music database RWC for research
purposes [6]. As shown, several local maximums of the fundamental frequency exist within the stream of the music. We have proposed a method for determining the region to be authenticated in
the audio data by using local maximums of the fundamental frequency of the audio data, as described in the next section.

5. PROPOSED METHOD FOR DETERMINING
THE REGION TO BE AUTHENTICATEDIN THE AUDIO DATA
The audio data clipping procedure for the authentication is as follows:
Step 1: The fundamental frequency f(i)(1, 2, 3, ..., n) at the start
time index i is measured every 0.01 s from the beginning to theend of T s of the original audio data, i.e., n = 100T. Then, the absolute value of difference ADF(i) = |f(i + 1) – f(i)| is calculated for alli(i = 1, 2, 3, ..., n – 1).
999•• F = {V′|V′ ∈ V′AC, V′ < Th′(minus)}

Step 2: The sum S(i) = å ADF (i + j) is calculated for all i(i = 1, 2, 3, ...,•• G = {V′|V′ ∈ V′AC, Th′(minus) ≤ V′ ≤ Th′(plus)}n – 999).

•• H = {V′|V′ ∈ V′AC, Th′(plus) < V′},

Step 3: The start time indexes i are determined by whether thevalue of S(i) is among the top 10 of all S(k) under the restriction|i – j| ≥ 1000 for all j such that S(i) < S(j).

where V′ is the set of wavelet coefficients from the target audio
data that is used to create the authentication code.
ACThe wavelet coefficients Vi¢ are then classified according to the following rules with the flags fi used in creating the original code C:
When fi = 1 and Vi¢ÎG , bi¢ is set to 0.

j=0

Step 4: For the start time indexes i selected in Step 3, 10 s of audio
data are clipped for the authentication.

6. EXPERIMENTS

When fi = 1 and Vi¢Î(F È H ), bi¢ is set to 1.

6.1. Conditions

When fi = 0, bi¢ is set to 0.5.
Note that the value 0.5 can be chosen arbitrarily, since the valueof bi that is the bit for creating the code for the original audio data
[1] does not influence the method’s performance. Finally, thissequence of bi¢ values is used to form the authentication code C′.

An experiment was performed in the following computational environment: the personal computer was a DELL OPTIPLEX CF-SX1

The authentication ratio AR(%) is defined as follows [Equation (1)]:N		

AR =100∑ fi (1− | bi − bi′ |)
i =1N∑ fi, (1)i =1where N is the number of wavelet coefficients chosen to create theauthentication code for the original audio data [1]. As can be seen inEquation (1), neither bi nor bi¢ influences the value of Authentication
Ratio (AR) when fi = 0, which occurs when the corresponding Vithat is the wavelet coefficient of the original audio is not selected forcoding in the original audio data [1].

Figure 2 | Fundamental frequency of audio data.

Y. Yoshitomi et al. / Journal of Robotics, Networking and Artificial Life 6(2) 133–137(CPU: Core i7-2600 Duo, 3.40 GHz; main memory: 4.0 GB); theOS was Microsoft Windows XP; the development language wasMicrosoft Visual C++ 6.0.

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by the lengths 0.00001, 0.00005, 0.0001, 0.0002, 0.0003, 0.0004,0.0005, 0.001, and 0.01 s. Then, the calculation of AR for the originalaudio data and the audio data after clipping by the above each length
was performed for one 10-s clip whose starting time from the beginning of the audio data was decided by the above method for the original audio data. For the DWT, we used Daubechies wavelets. Level 8was chosen based on an analysis of preliminary experiments [1].

Five music audio files, namely, the first entry of each of five genre
categories—classical, jazz, popular, rock, and hiphop—in the music
database RWC used for research purposes [6], were copied from CDs
onto a personal computer as WAVE files with the following specifications: 44.1 kHz, 16 bits, and monaural. For each music audio fileselected from the database, 10 sets of 10-second clips of music audio
were produced using the proposed method described in Section 5.

6.2. Results and Discussion
Tables 1 and 2 respectively show the AR values for AMFC and theHAR values for the proposed method. As shown in Table 1, AMFC
showed poor authentication tolerance to clipping: clipping 0.01 s ofaudio caused AR to decline to 65.88%. On the other hand, as shownin Table 2, the authentication tolerance to clipping of the audio data
was remarkably improved by adopting the proposed method.

For investigating the authentication tolerance to clipping, audio test
data were produced by clipping one region from each music audio
file selected from the database. The clipped regions were specified by
all combinations of the lengths 0.01, 0.1, 0.5, 1.0, 2.0, 3.0, and 4.0 s
and the starting times 0, 5, and 10 s (from the beginning of the original audio data), resulting in 21 clipping conditions. Then, for eachaudio test data file produced from each original audio data file, 10
sets of 10-s clips of music audio were produced using the proposed
method described in Section 5, and the authentication procedure
was performed using the previously proposed method [1]. The highest value of the authentication ratio of the original audio data to theaudio test data (hereinafter referred to as HAR) of AR as described inSection 3 among those of the 100 combinations of the 10 clips of theoriginal data and the 10 clips of test data was calculated.

Table 1 | AR with AMFC for clipping from thebeginning of original classical audio data

As another method for comparison (hereinafter referred as AMFC),
we chose audio data from the classical music genre category and produced one 10-s clip whose starting time gave the highest fundamental
frequency for 0.01 s among those in the audio data. Next, the clipping
regions from the beginning of the original audio data were specified

Clipping length (s)AR (%)

0.00001
0.00005
0.00010.00020.00030.00040.0005
0.001
0.01

100
100
99.22
96.84
87.75
84.98
78.66
69.57
65.88

Table 2 | HAR (%) with the proposed method under the 21 clipping conditions. Starting time for thetest data from the beginning of the original audio data: (a) 0, (b) 5, and (c) 10 s
Music
Classical
Clipping length (s)

0.01
0.1
0.5
1.0
2.0
3.0
4.0

100
100
98.82
98.08
73.12
67.19
94.25

Clipping length (s)

0.01
0.1
0.5
1.0
2.03.0
4.0

95.62
96.16
82.75
91.78
86.03
79.73
64.38

Clipping length (s)

0.01
0.1
0.5
1.0
2.0
3.0
4.0

100
96.44
97.65
93.70
88.77
80.00
64.84

(a)

(b)

(c)

Jazz

PopularRock

Hiphop

100
100
99.60
98.81
86.67
89.33
90.40

100
100
99.17
92.24
99.17
100
95.01

100
100
99.61
98.43
95.29
93.37
69.41

100
85.49
78.04
80.63
80.63
80.63
71.76

98.82
94.12
89.02
80.63
74.67
72.19
71.76

96.40
92.52
93.91
77.25
75.10
85.77
76.28

99.72
87.06
84.71
77.47
95.44
77.65
74.12

94.90
90.91
83.40
75.29
73.52
86.17
73.12

100
100
99.61
86.27
66.40
73.33
73.87

100
100
98.42
96.08
91.70
80.29
79.45

100
99.61
97.63
95.65
93.73
87.84
79.61

100
100
99.60
94.47
88.54
91.70
76.28

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Furthermore, when 5-s clip was used for narrowing the 10-s clip
decided as the region to be authenticated in the audio data, HAR
was much improved, being almost 100% for all the clipping conditions used in this experiment. Even for a clipping length of 4.0 s,HAR improved to 100% except under one condition for classicalaudio data: clipping from a starting time of 5 s from the beginningof the original audio data. In the exceptional case, HAR was 76.25%.

REFERENCES

7. CONCLUSION[3]In general, there is a tradeoff between the quality of watermarked
audio and the tolerance of watermarks to signal processing methods, such as compression. To overcome this inevitable tradeoff, wepreviously developed an authentication method [1] for digital audio
using a DWT. In the present study, we have improved the methodby determining the region to be authenticated in the audio file byusing the fundamental frequency characteristics. The experimental
results show that the method has a high authentication tolerance toclipping small parts from the audio data.

CONFLICTS OF INTEREST

[1]
[2]

[4]
[5]
[6]

Y. Yoshitomi, T. Asada, Y. Kinugawa, M. Tabuse, An authentication method for digital audio using a discrete wavelet transform,
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S. Murata, Y. Yoshitomi, H. Ishii, Optimization of embedding
position in an audio watermarking method using wavelet transform, in: Abstracts of The 2007 Fall National Conference of ORSJ,Tokyo, Japan, 2007, pp. 210–211 (in Japanese).
D. Inoue, Y. Yoshitomi, Watermarking using wavelet transformand genetic algorithm for realizing high tolerance to image compression, J. IIEEJ 38 (2009), 136–144.
T. Taniguchi, Y. Yoshitomi, Method for character domain
extraction from image using wavelet transform, J. Robot. Netw.
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R. Fujii, Y. Yoshitomi, T. Asada, M. Tabuse, An authentication
method using a discrete wavelet transform for a recaptured video,
J. Robot. Netw. Artif. Life 3 (2016), 107–110.
M. Goto, H. Hashiguchi, T. Nishimura, R. Oka, RWC music
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The authors declare they have no conflicts of interest.

Authors Introduction
Dr. Yasunari Yoshitomi

Mr. Masaki Arasuna

He received his B.E, M.E. and PhD
degrees from Kyoto University in 1980,
1982 and 1991, respectively. He works asa Professor at the Graduate School of Lifeand Environmental Sciences of Kyoto
Prefectural University. His specialties areapplied mathematics and physics, informatics environment, intelligent informatics. IEEE, HIS, ORSJ, IPSJ, IEICE,SSJ, JMTA and IIEEJ member.

Mr. Shohei Tani

He received his B.S. degree from Kyoto
Prefectural University in 2017. He works at
Nissay Information Technology Co., Ltd.

Mr. Ryota Kan
He received his B.S. degree from Kyoto
Prefectural University in 2013. He works
at Fukuchiyama City Hall.

He received his B.S. degree from Kyoto
Prefectural University in 2017. He works
at Shimazu Business Systems Co., Ltd.

Y. Yoshitomi et al. / Journal of Robotics, Networking and Artificial Life 6(2) 133–137

Dr. Taro Asada137

Dr. Masayoshi Tabuse
He received his B.S., M.S. and PhD degrees
from Kyoto Prefectural University in 2002,
2004 and 2010, respectively. He works as
an Associate Professor at the Graduate
School of Life and Environmental Sciencesof Kyoto Prefectural University. His current research interests are human interface
and image processing. HIS, IIEEJ member.

He received his M.S. and PhD degrees
from Kobe University in 1985 and1988 respectively. From June 1992 toMarch 2003, he had worked in Miyazaki
University. Since April 2003, he has been
in Kyoto Prefectural University. He worksas a Professor at the Graduate Schoolof Life and Environmental Sciences ofKyoto Prefectural University. His current research interests aremachine learning, computer vision and natural language processing. IPSJ, IEICE and RSJ member.