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

Abstract

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/).

Effective blind speech watermarking via adaptive mean modulation and package synchronization in DWT domain

“…This study compared the performance of three wavelet transform (WT)-based speech watermarking methods, namely, DWT-SVD [4], LWT-DCT-SVD, [3] and the proposed DWT-AMM. For the sake of a fair comparison, the watermark bits were embedded in the second-level approximation subband using an identical payload capacity of 200 bps for all three methods.…”

“…Speech is a specific form of audio signal; therefore, the techniques developed for audio watermarking are presumed to be applicable to speech watermarking. However, speech differs from typical audio signals with regard to spectral bandwidth, intensity distribution, signal continuity, and production modeling [3,4]. The techniques developed for audio watermarking are not necessarily suitable for speech watermarking [5].…”

Digital speech watermarking for anti-spoofing attack in speaker recognition

“…In this paper, our proposed digital speech watermarking [13] has been used for anti-spoofing attack. Figure 2 shows proposed system in transmitter side.…”

“…As seen, first the speech signal is checked for available watermark, if watermark is available in speech signal, it means the signal is already has been used (replay attack). Otherw embedded in speech signal as anti-spoofing at In this part, our paper has been briefly expl anti-spoofing purpose [13]. A digital speech w been proposed based on singular value decom and discrete wavelet transform (DWT) [13].…”

Speech Recognition with Advanced Feature Extraction Methods Using Adaptive Particle Swarm Optimization

“…Digital speech is different from audio signal in respect to factors like production model, perception, bandwidth, loudness, and intensity. Digital watermarking is the proper technique to protect and monitor the digital media [10]. In the recent years, the emotion recognition from speech has noticeable applications in the speech-processing systems, such as spoken tutoring systems, medical emergency domain to detect stress and pain, interactions with robots, computer games, and call centers [11].…”