An objective method for evaluating datahiding in pitch gain and pitch delay
parameters of the AMR codec
Akira Nishimura1
1 Department of Media and Cultural Studies, Tokyo University of Information Sciences, Chiba, Japan

Correspondence should be addressed to Akira Nishimura (akira@rsch.tuis.ac.jp)
ABSTRACT
Audio data hiding technology has several applications in the field of distribution, communication, and audio
data trading. Steganographic use of audio data hiding enhances the quality and quantity of audio data
communication. On the other hand, embedding hidden data may degrade the perceptual quality of theaudio signal. Three methods for hiding data in pitch-related parameters of the advanced multi rate (AMR)
narrow-band speech codec were evaluated in terms of the objective quality degradation and the bit rate of theembedding data. Computer simulations of the data hiding system were conducted for the AMR 12.2-kbps
and 7.95-kbps modes. The results revealed that the method of replacing the least significant bit (LSB) of thepitch gain parameter with the information bits was superior in terms of embedding bit rate and less soundquality degradation than other methods, which use LSBs of the pitch delay data.

1. INTRODUCTION
The most general application of audio data hiding technology is copyright protection of the audio data, which iscalled watermarking. Watermarking technology requires
robustness with respect to modifications of the watermarked audio signals caused by transmission via various audio media. The modifications are, for example,
transcoding by perceptual audio codecs, AD/DA conversions, additive noises, low-pass filtering, and malicious
modification attacks for piracy distribution. The size ofwatermarking data can be small, because the copyrightor authentication data is coded efficiently.
Another essential application is steganography, which involves embedding additional data that may or may not
be related to the contents of the audio data. Since theembedded data is usually not audible and the human listener is unaware of its existence, the data can be used toenhance the quality and quantity of audio data communication. In such applications, the embedded data can
include annotation and semantic description of the audio data, multimedia data, bandwidth extension or packet
loss concealment of the speech codec, and hidden channel communication. The size of the additional data isrequired to be as large as possible in order to increase therange and efficacy of the application.

The most important issue in both watermarking andsteganography technologies is the perceptual transparency of the embedded audio signal. In other words,
no perceptual quality degradation should be found in theembedded audio signal.
Data hiding in speech data encoded by a speech codec
has been considered to be useful for steganography inorder to enhance speech communication. A number ofmethods have been proposed to embed hidden data into
encoded speech data.
Most of these studies performed objective measurementof speech quality degradation using segmental signal-tonoise ratio (SNR), which exhibits the level of the reference speech signal relative to the level of the noise components induced by transcoding in short segments. However, modern Code Excited Linear Prediction (CELP)
based speech codecs, such as LD-CELP (ITU-T Rec.
G.728) , CS-ACELP (ITU-T Rec. G.729), and Advanced
Multi Rate (AMR) codecs [1], reconstruct perceptual oriented speech waveforms and have relatively small SNRsof approximately 10 dB. Consequently, a small difference in SNR obtained between the standard codec andthe modified codec for data hiding does not truly reflecta small perceptual difference between two codecs.

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Perceptual evaluation for speech quality (PESQ) is an alternative method of objective sound quality evaluation
for speech codecs that is recommended in ITU-T Rec.
P.862 [2]. PESQ compares an original signal with a signal that has been degraded by passing through a communications system. The key to this process is the transformation of both the original and degraded signals to
an internal representation that is analogous to the psychophysical representation of audio signals in the human
auditory system, taking into account the perceptual frequency (Bark) and loudness (Sone). The transformed
output of PESQ, which is defined in ITU-T Rec. P.862-1,
is called the mean opinion score listening quality objective (MOS-LQO) and corresponds to the results of mean
opinion score listening quality subjective (MOS-LQS)
obtained from human listeners by the subjective experiments.

the 12.2-kbps and 7.95-kbps modes, the pitch gain andthe codebook gain are separately quantized. In othermodes, moving averaged prediction from the previous
frames and vector quantization are applied to the combined pitch and codebook gain parameters (see the bottom of Figure 1). Except for the 4.75-kbps and 5.15kbps modes, the pitch delay parameters of the secondand fourth subframes are represented as the difference
from the nearest integer value of the pitch delay of theprevious subframe.
Mode(kbps)
12.2

In the present paper, least significant bit (LSB) based
data hiding methods in pitch delay or pitch gain parameters of the AMR codec are evaluated in terms of thecapacity of hidden data and the objective quality of decoded speech signals.

10.2

2. AMR NARROW-BAND SPEECH CODEC

7.95

A large number of 3rd Generation Partnership Project(3GPP) based cellular phones adopt the AMR speech
codec. The encoder converts 20 ms of an 8-kHz and 13bit digital waveform frame into Line Spectral Pair (LSP)
parameters, pitch parameters, algebraic code index, andgain parameters. These parameters are transmitted using
the selective bit rate mode from 4.75 to 12.2 kbps. The
coding scheme for the multi-rate coding modes is the Algebraic Code Excited Linear Prediction (ACELP) coder[3].
A simplified block diagram of the encoding process is
depicted in Fig. 1. At first, spectral features of the framed
speech signal are quantized as LSP parameters. Then,
pitch analysis extracts the pitch delay of the waveform
and the gain of the periodical excitation. Finally, thecombination of the algebraic pulse positions, their polarities, and a gain are suitably selected by minimizing
the residual excitation of the remainder of the periodical
pitch excitation in the speech waveform.
Table 1 shows the bit allocation of the AMR coding algorithm for the three typical modes. LSP parameters are
encoded once for every 20-ms frame and the other parameters are encoded once for every 5-ms subframes. In

Parameter
2 LSP sets
Pitch delay
Pitch gain
Algebraic code
Codebook gain
LSP setPitch delay
Algebraic code
Gains
LSP setPitch delay
Pitch gain
Algebraic code
Codebook gain

1st
9
4
35
5
8
31
7
8
4
17
5

subframes
2nd 3rd
38
6
9
4
4
35
35
5
5
26
5
8
31
31
7
7
27
6
8
4
4
17
17
5
5

4th
6
4
35
5
5
31
7
6
4
17
5

Table 1: Bit allocation of the AMR codec.
3. DATA HIDING IN ENCODED SPEECH DATA
3.1. General methods for embedding
Several methods have been proposed to embed hidden
data into the encoded speech parameters. Although embedding data into the LSP parameters [4] may be robust
against DA/AD conversion, the sound quality is severely
degraded. Embedding data into the fixed codebook index
by selecting a labeled codebook table is effective in thehigh-bit- rate mode [5, 6], because several bit allocations
for the codebook table make the fixed pulse positions redundant. These techniques inevitably require integrationof the embedding unit and the standard speech encoder.
In the present study, the embedding methods in pitch delay and pitch gain parameters are examined. Quantized
pitch delay and pitch gain parameters simply correspondto the physical quantities, the fundamental period (inverse of frequency), and the intensity of the voiced partof the speech signal. Therefore, embedding in the bit

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SpeechsignalPitch search
Open loop Closed loop
searchsearchPre-filtering

Find best
gain
Fixed codebook search
Codebook Find best
selection
gain

LPC analysis
LSP conversion
SVQ

Encoder outputs

Q

12.2, 7.95 kbps
modes
All modes

LSP indices

Pitch index

10.2, 7.4, 6.7, 5.9,
5.15, 4.75 kbps modes
SVQ Split Vector Quantizer

Q

Q

Pitch gain
index

Codebook
gain index

VQ

Codebook
index

Gain index
VQ Vector Quantizer

Q

Quantizer

Fig. 1: Simplified block diagram of the AMR encoder.
stream output of the standard AMR encoder can be implemented while presuming quality degradation causedby modifying the bit value at a suitable location. Thistakes advantage of the use of the standard encoder andan additive embedding unit posterior to the standard encoder.
3.2. Methods for embedding in pitch parameters
Three methods of data hiding into the pitch related dataof the AMR codec are evaluated in terms of embedding
data capacity and objective sound quality.
Iwakiri proposed a method of hiding data in a LSB of thepitch delay parameter in an ITU-T Rec. G.723.1 based
speech codec. Replacing the LSB of the quantized pitch
delay data with hidden data for every subframe achieved
an embedding rate of 134 bps. If the voice activity detection (VAD) and discontinuous transmission (DTX) functions of the AMR codec are not activated, embedding all
5-ms subframes achieves a maximum embedding rate of
200 bps. This method is hereafter referred to as the pitch
LSB (PLSB) method.
Sasaki et al. proposed data hiding in the pitch delay parameter based on the pitch gain value in a CELP based
speech codec [7]. If the pitch gain value is less than
the threshold value, the apparatus embeds hidden data
by replacing the LSBs of the pitch delay data. Assigning a higher threshold value and a wider bit width of the
LSB increases the capacity of the embedding data. Thismethod is hereinafter referred to as the gain threshold
pitch LSB (GTPLSB) method. The GTPLSB method canbe simply applied to the AMR encoder for the 12.2-kbps
and 7.95-kbps modes because these modes have separate
pitch gain parameters. In other modes, however, vector
quantization is performed jointly using the pitch gain parameter and the codebook gain parameter, which is predicted from previous frames. The embedding algorithm
may be rather complex and computationally overloaded,
except for the 12.2-kbps and 7.95-kbps modes. For thisreason, only the 12.2-kbps and 7.95-kbps modes were
evaluated in the present paper.
Another simple method of embedding data into the pitch
data is LSB replacement of the pitch gain data. In thesame way as for the PLSB, embedding all subframes
achieves a maximum embedding rate of 200 bps. Thismethod is hereinafter referred to as the pitch gain LSB(PGLSB) method. For the same reason as for the GTPLSB, the 12.2-kbps and 7.95-kbps modes were tested.
Figure 2 shows the data hiding system for a speech codec
via a phone network. The above three methods can be
implemented to either modify the standard encoding algorithm (see Fig.2 Integrated implementation) or modify
the output bit stream of the standard encoder (see Fig.2
Separated implementation). The latter case has the advantage of a simple structure. The former case is considered to be advantageous for the PLSB. Implementing theembedding algorithm in the closed pitch search section
allows the fixed codebook search section to optimize and

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reduce residual errors caused by modification to the pitch
delay value. Other methods have this advantage only inthe 12.2-kbps mode because other modes determine thequantized pitch gain values depending on the process ofthe fixed codebook and gain search (not shown in Fig. 1
for simplicity). In the following section, which describes
the computer simulation, the effect of this optimization
is examined by comparison between the results of integrated and separated implementations.

Speech signal(A): Integrated implementation
Modified encoder withembedding unit
Standard encoder(B): Separated implementation

Auxiliary data
TX-SCR
Embedding unit

Public network
RX-SCR

Standard decoder
Extraction unit

Speech signal

Auxiliary data

TX: Transmit
RX: Receive
SCR : Source controlled rate operation: Bit stream

Fig. 2: Data hiding system for a speech codec via aphone network..
3.3. Extraction of embedded data from encoded speech data
Extraction of the embedded data from the encoded
speech data is rather simple compared with extracting therobust watermark from the music signals. The bit streamof the input of the speech decoder is analyzed to find hidden bit locations according to the embedding rule. No
modification to the standard speech decoder is required(Fig.2). The bit stream including hidden data bits is sent
to the standard speech encoder at the same time. Thismay cause quality degradation of the decoded speech signals. Therefore, an important method to implement datahiding in encoded speech data is to locate bit locations
that are not significant from a perceptual standpoint.
4. COMPUTER SIMULATION

catenating two sentences from 1,100 sentences selectedfrom the Continuous Speech Database for Research (Vol.
1) published by the Acoustical Society of Japan. The
duration of the speech ranged from 6 to 12 seconds, including silence intervals. The overall level of each input
speech signal was –26 dBov. Then, the output bit streamof encoded speech data was fed into the standard AMR
decoder. PESQ software distributed by ITU-T was applied to the decoded speech signal.
In addition, the reference speech signals were fed into
the standard AMR encoder and decoder, which is distributed by 3GPP organization partners [8], and thePESQ software. The MOS-LQO difference in the decoded speech signal between data hiding and the standard AMR transcoding is considered to be a measureof the quality degradation. Negative values indicate theamount of quality degradation induced by data hiding.
The embedding methods tested herein were the PLSB,GTPLSB, and PGLSB methods. The embedding bit rate
was set to two levels, which were below 100 bps and 200
bps. The embedding bit rate is able to be roughly controlled by selecting appropriate values of the embedding
parameters, that is, the number of embedding subframes,
the width of the LSB, and the pitch gain threshold. The
parameter values are shown in Table 2. Since activating
the VAD and DTX functions in the encoder resulted in
no data being embedded in the frames of the non-speech
signal, the embedding bit rate depended somewhat on thelength of the silence intervals in each speech sound file.
Parameters
EmbeddingMethod LSB Subframes Threshold bit rate [bps]
PLSB
1
2, 4
—
max. 1001
1,2,3,4—max. 200
GTPLSB 2
1,2,3,4
3
50 — 1203
1,2,3,4
4
100 — 220
PGLSB
1
2, 4
—
max. 1001
1,2,3,4—max. 200

4.1. Measurement of quality degradation using
PESQ
PESQ was adopted to evaluate the objective quality
degradation caused by data hiding in the reference
speech signals obtained by 16-bit quantization and 8-kHz
sampling. A total of 550 phonetically balanced sentences
spoken by 22 Japanese speakers (12 men and 10 women)
were fed into the input of the AMR encoder with an embedding unit. These sentences were generated by con-Table 2: Simulation parameters.
4.2. Results
The results of the computer simulation are shown as a
two-dimensional map, where the abscissa and the ordinate denote the rate of embedding bit and the differenceof MOS-LQO, respectively. Each dot in the figure represents a speech signal used for testing.

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MOS-LQO difference
modified codec - standard codec

Evaluation of data hiding in the AMR codec

1-4 subframes
2,4 subframes
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

60

80 100 120 140 160 180 200 220
Embedding rate [bps]Fig. 3: Quality degradation induced by data hiding versus embedding data bit rate. Integrated PLSB was employed for the 12.2-kbps AMR mode.

MOS-LQO difference
modified codec - standard codec

Figures 3, 4, and 5 show the results of integrated embedding implementation for PLSB, GTPLSB, and PGLSB,
respectively, at a speech data bit rate of 12.2 kbps. Figure
6 shows the result of integrated PLSB at the 7.95-kbps
mode.
Gain threshold: 4, 3 LSBs
Gain trehsold: 3, 2 LSBs
0
-0.1
-0.2
-0.3

0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

60

80 100 120 140 160 180 200 220
Embedding rate [bps]1-4 subframes
2,4 subframes
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

60

80 100 120 140 160 180 200 220
Embedding rate [bps]Fig. 6: Quality degradation induced by data hiding versus embedding data bit rate. Integrated PLSB was employed for the 7.95-kbps AMR mode.

-0.4
-0.5
-0.6

1-4 subframes
2,4 subframes

Fig. 5: Quality degradation induced by data hiding versus embedding data bit rate. Integrated PGLSB was employed for the 12.2-kbps AMR mode.
MOS-LQO difference
modified codec - standard codec

MOS-LQO difference
modified codec - standard codec

NISHIMURA

60

80 100 120 140 160 180 200 220
Embedding rate [bps]Fig. 4: Quality degradation induced by data hiding versus embedding data bit rate. Integrated GTPLSB was
employed for the 12.2-kbps AMR mode.
Figures 7, 8, and 9 show the results of separated embedding implementation for PLSB, GTPLSB, and PGLSB,
respectively, at a speech data bit rate of 12.2 kbps. Compared with the integrated implementation, the amountof sound quality degradation was clearly increased forthe separated PLSB method. Comparison between integrated and separated implementation was also conducted for GTPLSB and PGLSB in the 12.2-kbps mode.

There was no significant difference observed between integrated implementation and separated implementation.
These results show that the integrated implementation isadvantageous only for the PLSB method.
The range of quality degradation of PGLSB is limited
and small. The corresponding t-test also shows thatthe mean MOS-LQO difference was the smallest for
PGLSB, as compared to the other methods of separated
implementation, in both data bit rate modes and higher
embedding bit rate conditions. In addition, the integrated PGLSB at the 12.2-kbps mode showed the smallest MOS-LQO difference among the other methods ofthe integrated implementation. In order to express the av-

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Evaluation of data hiding in the AMR codec

MOS-LQO difference
modified codec - standard codec

MOS-LQO difference
modified codec - standard codec

NISHIMURA

1-4 subframes
2,4 subframes
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

60

80 100 120 140 160 180 200 220
Embedding rate [bps]Gain threshold: 4, 3 LSBs
Gain trehsold: 3, 2 LSBs
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

60

80 100 120 140 160 180 200 220
Embedding rate [bps]Fig. 8: Quality degradation induced by data hiding versus embedding data bit rate. Separated GTPLSB was employed for the 12.2-kbps AMR mode.
erage differences among all conditions at a glance, Fig.
10 shows the mean MOS-LQO difference and ± 1 standard deviation for all conditions.
The range of quality degradation of GTPLSB is comparable to that of PGLSB. However, the range of embedding bit rate is diverse and presents a disadvantage forpractical data hiding applications.
In summary, PGLSB yields superior results in both theseparated and integrated implementations.
5. DISCUSSION
The embedding bit rates for PGLSB and PLSB dependon the duration of silence or non-speech intervals in the

0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

60

80 100 120 140 160 180 200 220
Embedding rate [bps]Fig. 9: Quality degradation induced by data hiding versus embedding data bit rate. Separated PGLSB was employed for the 12.2-kbps AMR mode.

Mean MOS-LQO difference
modified codec - standard codec

MOS-LQO difference
modified codec - standard codec

Fig. 7: Quality degradation induced by data hiding versus embedding data bit rate. Separated PLSB was employed for the 12.2-kbps AMR mode.

1-4 subframes
2,4 subframes

0High embedding bit rate, 12.2-kbps mode
Low embedding bit rate, 12.2-kbps mode
High embedding bit rate, 7.95-kbps mode
Low embedding bit rate, 7.95-kbps mode

-0.1
-0.2
-0.3
-0.4
-0.5

PLSB

GTPLSB PGLSB
Integrated

PLSB

GTPLSB PGLSB
Separated

MethodFig. 10: Mean MOS-LQO difference for all conditions.
Error bars denote ± 1 standard deviation.
speech signal. If the noisy condition is simulated, theembedding bit rate will increase slightly for both PLSB
and PGLSB. The embedding bit rates of GTPLSB in the
noisy condition will clearly increase, because the ratio of
periodic components in the speech waveform decreases
in the noisy condition. Informal simulation revealed thatembedding bit rate increases from 10% to 20% depending on the SNR.
The integrated implementation is advantageous only for
the PLSB method. The reason why the integrated implementation is not effective for the other methods is asfollows: The errors induced by PLSB embedding are reduced by optimizing the three parameters in the encoder,

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the pitch gain, the fixed codebook index, and the fixed
codebook gain. On the other hand, the number of the
optimized parameters are two, the fixed codebook index
and the fixed codebook gain, for GTPLSB and PGLSB.
Balancing optimization between the pitch gain and thecodebook-related parameters may be effective to reduce
errors in the pitch delay data. Another reason is localization of the pitch delay errors of GTPLSB. The GTPLSB
method replaces LSBs of the pitch delay data where thepitch gain is small, that is, where non-periodic speech
signal is observed. The errors of the pitch delay data insuch region do not affect the perceptual quality of the
speech signal.
The present study dealt with objective evaluation of thethree data hiding methods for the AMR narrow-band
speech codec. Subjective evaluation is also useful for
confirming the present results, whereas a great deal ofeffort is required for subjective experiments. Most subjective evaluations for data hiding in speech codecs conducted in previous studies used the absolute category
rating (ACR) method, in which the listeners performed
evaluation using absolute categories of excellent, good,
fair, poor, and bad, which corresponds to nominal values of five to one. The ACR method is not suitable fordiscovering subtle sound quality degradations. A general method for measuring perceptual transparency is the
double-blinded AXB discrimination test. Giving that theperceptual difference is clear between the standard codec
and the modified codec, however, it does not mean thatthe sound quality of the modified codec is inferior tothat of the standard codec. An adequate method to rate
the sound quality of the modified codec compared withthat of the standard codec is pair or multiple degradationcomparison test, such as MUltiple Stimuli with Hidden
Reference and Anchor (MUSHRA) method, as specified
in ITU-R Rec. BS.1534-1.
The simple algorithms of the GTPLSB and PGLSB
methods are limited to the AMR 12.2-kbps and 7.95kbps modes. PGLSB is difficult to extend to other data
bit rate modes in the present form because the pitch gain
parameter is not separately quantized in the output bit
stream of the encoder. The advantages of the LSB based
data hiding method are simplicity and a computationallylight load. Extension and improvement of the LSB methods for other bit rate modes, while maintaining the advantages of these methods, should be examined in the
future.

6. SUMMARY
Three methods for data hiding in pitch-related parameters of the AMR narrow-band speech codec were evaluated in terms of the objective quality degradation and bit
rate of embedding data. Computer simulation of the datahiding system revealed that the method of replacing the
LSB of the pitch gain parameter in information bits was
far superior to the other methods, which use the LSBs of
the pitch delay data. The present method and simulation
were conducted for the AMR 12.2-kbps and 7.95-kbps
modes. Extension to other bit rate modes should be examined in the future.
Acknowledgments
The present research was supported in part by the Collaboration Research Program No. 4 of Tokyo Universityof Information Sciences 2007, 2008 and by KAKENHI,
20560365.
7. REFERENCES[1] 3rd Generation Partnership Project, “Mandatory
Speech Codec speech processing functions AMR
Speech Codec; General Description,”
26.071,(2001).
[2] ITU-T Recommendation, “Perceptual evaluation of
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[3] 3rd Generation Partnership Project, “Mandatory
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[4] HATADA Mitsuhiro, SAKAI Toshiyuki, KOMATSU Naohisa, and YAMAZAKI Yasushi, “A
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Wideband Telephony in Mobile Networks: CELPAES JAPAN CONFERENCE IN OSAKA, New ABC Hall, Osaka, 2008 July 25–26
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