An enhanced psychoacoustic model based on the discrete wavelet packet transform

He, Xiao; Scordilis, Michael S.

doi:10.1016/j.jfranklin.2006.07.005

Cited by 19 publications

(19 citation statements)

References 15 publications

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“…While studying spectral masking, critical bands are of great importance because they can be employed to elucidate the properties of frequency selectivity [32,33]. Based upon the theory of perceptual entropy [31][32][33][34][35], this study derives the auditory masking threshold in terms of signal power for each critical band.…”

Section: Derivation Of Auditory Masking Threshold In the Dwpt Domainmentioning

confidence: 99%

“…Based upon the theory of perceptual entropy [31][32][33][34][35], this study derives the auditory masking threshold in terms of signal power for each critical band. The derivation begins with the utilization of the DWPT to approximate the critical bands.…”

Section: Derivation Of Auditory Masking Threshold In the Dwpt Domainmentioning

confidence: 99%

“…Because the DWPT possesses multi-resolution capacity and is more computationally efficient than the Fourier transform, it may cooperate with the psychoacoustic model to render an estimate of auditory masking thresholds [31,32]. Hence, our aim in this study is to explore all useful properties of the DWPT, SVD, and QIM for audio watermarking such that the issues of robustness, imperceptibility, and payload capacity can be resolved altogether.…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking

Chou

et al. 2014

EURASIP J. Adv. Signal Process.

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This paper presents a novel approach for blind audio watermarking. The proposed scheme utilizes the flexibility of discrete wavelet packet transformation (DWPT) to approximate the critical bands and adaptively determines suitable embedding strengths for carrying out quantization index modulation (QIM). The singular value decomposition (SVD) is employed to analyze the matrix formed by the DWPT coefficients and embed watermark bits by manipulating singular values subject to perceptual criteria. To achieve even better performance, two auxiliary enhancement measures are attached to the developed scheme. Performance evaluation and comparison are demonstrated with the presence of common digital signal processing attacks. Experimental results confirm that the combination of the DWPT, SVD, and adaptive QIM achieves imperceptible data hiding with satisfying robustness and payload capacity. Moreover, the inclusion of self-synchronization capability allows the developed watermarking system to withstand time-shifting and cropping attacks.

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Section: Derivation Of Auditory Masking Threshold In the Dwpt Domainmentioning

confidence: 99%

Section: Derivation Of Auditory Masking Threshold In the Dwpt Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking

Chou

et al. 2014

EURASIP J. Adv. Signal Process.

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“…Figure 2 shows how the signal bandwidth is decomposed to 19 relatively critical subbands in the region of 0-7 kHz. The difference between our method and other similar ones is the combination of WP outputs in different levels of wavelet expansion tree wherever a perfect match with the critical bands is not possible (sub-bands [14][15][16][17][18][19]. These 19 critical sub-bands are summarized in Table 1 used the new wavelet kernel exploited in [6].…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Ghahabi

Savoji

2011

ISRN Signal Processing

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A fast, efficient, and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-k zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising (1) embedded zero-tree wavelet (EZW) and (2) the set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times.

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“…Such approach greatly limits the application of these techniques. Although wavelet analysis in psychoacoustic model computation has been recently explored [12][13][14][15], it is either computationally expensive [12,13] by having to rely on the Fourier transforms for the computation of the psychoacoustic model itself, or their critical bands approximation deviate from the standard partition by varying degrees [14], which may result in objectionable audible distortion in the reconstructed signal.…”

Section: Introductionmentioning

confidence: 99%

Efficiently Synchronized Spread‐Spectrum Audio Watermarking with Improved Psychoacoustic Model

Scordilis

2008

Journal of Electrical and Computer Engineering

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This paper presents an audio watermarking scheme which is based on an efficiently synchronized spread-spectrum technique and a new psychoacoustic model computed using the discrete wavelet packet transform. The psychoacoustic model takes advantage of the multiresolution analysis of a wavelet transform, which closely approximates the standard critical band partition. The goal of this model is to include an accurate time-frequency analysis and to calculate both the frequency and temporal masking thresholds directly in the wavelet domain. Experimental results show that this watermarking scheme can successfully embed watermarks into digital audio without introducing audible distortion. Several common watermark attacks were applied and the results indicate that the method is very robust to those attacks.

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An enhanced psychoacoustic model based on the discrete wavelet packet transform

Cited by 19 publications

References 15 publications

Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking

Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Efficiently Synchronized Spread‐Spectrum Audio Watermarking with Improved Psychoacoustic Model

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