An overview of digital speech watermarking

Nematollahi, Mohammad Ali; Al-Haddad, S. A. R.

doi:10.1007/s10772-013-9192-6

Cited by 39 publications

(11 citation statements)

References 63 publications

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“…Although there is no standard definition available for this phase, three definitions have been used frequently in various references [17]. These definitions include the autoregressive (AR) phase, DFT phase, and lapped orthogonal transforms (which has no direct phase manipulation).…”

Section: Transform Domainmentioning

confidence: 99%

Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

Nematollahi¹,

Vorakulpipat²,

Gamboa-Rosales³

2017

Mathematical Problems in Engineering

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There are various techniques for speech watermarking based on modifying the linear prediction coefficients (LPCs); however, the estimated and modified LPCs vary from each other even without attacks. Because line spectral frequency (LSF) has less sensitivity to watermarking than LPC, watermark bits are embedded into the maximum number of LSFs by applying the least significant bit replacement (LSBR) method. To reduce the differences between estimated and modified LPCs, a checking loop is added to minimize the watermark extraction error. Experimental results show that the proposed semifragile speech watermarking method can provide high imperceptibility and that any manipulation of the watermark signal destroys the watermark bits since manipulation changes it to a random stream of bits.

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Section: Transform Domainmentioning

confidence: 99%

Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

Nematollahi¹,

Vorakulpipat²,

Gamboa-Rosales³

2017

Mathematical Problems in Engineering

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“…Watermarking on speech signal is discussed in this section (further discussions can be found in our previous (Nematollahi and Al-Haddad, 2013)). …”

Section: Watermarkingmentioning

confidence: 99%

“…The digital audio watermarking techniques proposed in various studies can be classified into five main categories: auditory masking, phase modulation, quantization, transformation, and parametric modeling (Nematollahi and Al-Haddad, 2013). However, in this paper, the watermark bits are embedded into the Eigen-value of Approximation Coefficient (AC) which is computed by Discrete Wavelet Transform (DWT).…”

Section: Introductionmentioning

confidence: 99%

Blind digital speech watermarking based on Eigen-value quantization in DWT

Nematollahi

Al-Haddad

Zarafshan

2015

Journal of King Saud University - Computer and Information Scie

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This paper presents a new blind digital speech watermarking technique based on Eigenvalue quantization in Discrete Wavelet Transform. Initially, each frame of the digital speech was transformed into the wavelet domain by applying Discrete Wavelet Transform. Then, the Eigenvalue of Approximation Coefficients was computed by using Singular Value Decomposition. Finally, the watermark bits were embedded by quantization of the Eigen-value. The experimental results show that this watermarking technique is robust against different attacks such as filtering, additive noise, resampling, and cropping. Applying new robust transforms, adaptive quantization steps and synchronization techniques can be the future trends in this field. ª 2014 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…While the prior art has been reviewed in previous studies [15,16], it would be useful to compile a summary of the more relevant developments in speech watermarking for analog and digital media. Therefore, the main studies in speech watermarking technology are discussed in this section.…”

Section: Literature Reviewmentioning

confidence: 99%

Semi-fragile digital speech watermarking for online speaker recognition

Nematollahi

Akhaee

Al-Haddad

et al. 2015

J AUDIO SPEECH MUSIC PROC.

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In this paper, a semi-fragile and blind digital speech watermarking technique for online speaker recognition systems based on the discrete wavelet packet transform (DWPT) and quantization index modulation (QIM) has been proposed that enables embedding of the watermark within an angle of the wavelet's sub-bands. To minimize the degradation effects of the watermark, these sub-bands were selected from frequency ranges where little speaker-specific information was available (500-3500 Hz and 6000-7000 Hz). Experimental results on the TIMIT, MIT, and MOBIO speech databases show that the degradation results for speaker verification and identification are 0.39 and 0.97 %, respectively, which are negligible. In addition, the proposed watermark technique can provide the appropriate fragility required for different signal processing operations.

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An overview of digital speech watermarking

Cited by 39 publications

References 63 publications

Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

Blind digital speech watermarking based on Eigen-value quantization in DWT

Semi-fragile digital speech watermarking for online speaker recognition

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