Signal Analysis Research (SAR) Group - RNet - Ryerson University

formant referenced by the black arrow. The watermark is em-
Normalized frequency
0.5
0.25
0
0 1000 2000 3000 4000
Normalized time
Fig. 3. Log–spectrogram of the test signal. Male utterance of
the word “bingo” with a sampling rate of 8 kHz.
bedded as described in Sec. 3.1. First, the data (τ)2 = 010011
is used to generate the amplitude of the watermark by means
of the Equ. 16. Then the insertion zone is manually choseninordertodene
the warping operator used to generate
the time–warped watermark. Finally, the time–warped watermark
is added to the original signal. Result of the watermark
embedding is shown in Fig. 4. As can be seen, the time–
Normalized frequency
0.5
0.25
0
0
1000
Normalized time
(a) Original signal
Normalized frequency
0.5
0.25
0
0
1000
Normalized time
(b) Watermarked signal
Fig. 4. Zoomed log–spectrogram of the original and watermarked
test signal.
warped watermark is very close to the original formant. As
expected, the frequency spread decreases very fast, thanks to
the smoothness of the amplitude of the watermark sequence.
We found the embedding strategy satisfactory since we
were not able to guess wether the signal was watermarked or
not during blind tests. In order to provide a more objective
comparison criterion, we make use of the “Auditory Toolbox”
[4] to generate auditory representations of original and
watermarked signals which is a pseudo–time–frequency representation
based on physiological aspects of human hears.
Auditory representations of original and watermarked signals
are depicted in Fig. 5. Both representations are very similar
which conrms stealthiness of the watermark.
Next step consists in the recovery of the watermark sequence.
We tested the proposed approach on the true watermarked
signal, and on two different deteriorated versions of
the watermarked signal : the rst is a MP3 compression attack,
and the second is an additive Gaussian noise attack with
a signal–to–noise ratio of 0 dB.
Results of the matched ltering estimation are presented
in Tab. 1. Results of the estimation step show that the watermark
is perfectly extracted and has resisted to the MP3 attack
as well as the white–noise attack.
II ­ 204
27
Channels
120
60
0
0
1000
Normalized time
(a) Original signal
Channels
120
60
0
0
1000
Normalized time
(b) Watermarked signal
Fig. 5. Auditory representation of the original signal and the
watermarked signal.
τ τ1 τ2 τ3 τ4 τ5 τ6
True 0 1 0 0 1 1
No attack 0 1 0 0 1 1
MP3 attack 0 1 0 0 1 1
Noise attack 0 1 0 0 1 1
Table 1. Results of the estimation of the set {τi} by matched
ltering.
5. CONCLUSION
In this paper we have proposed a new watermarking method
for speech signals, based on the time–warping signal processing
concept. We have shown that it is possible to exploit physiological
aspects of the human hear in order to carry information
while keeping stealthiness of the inserted watermark.
Then, we have developed a complete extraction method based
on time–varying lter, time–warping operators and match ltering,
to recover the watermark sequence. Numerical results
show that the watermark is likely not hearable and numerical
information carried by the watermark are retrievable. Future
work will include a close study the robustness of the method
against various attacks. For real applications, another topic
is the unsupervised embedding of the watermark according to
the position of formant. This issue is left for future work.
6. REFERENCES
[1] M. Arnold, “Audio watermarking: Features, applications
and algorithms,” in IEEE International Conference on
Multimedia and Expo, New York, USA, July 2000.
[2] R. Baraniuk, “Unitary equivalence: A new twist on signal
processing,” IEEE Trans. on Signal Processing, vol. 43,
no. 10, pp. 2269–2282, Oct. 1995.
[3] M.C. Botte, G. Canevet, L. Demany, and C. Sorin, Psychoacoustique
et perception auditive, Inserm, 1989.
[4] M. Slanley, “Auditory toolbox, version 2.0,” Avaiable at
http://www.slaney.org/malcolm/pubs.html, 1994.
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