Robust audio watermarking based on transform domain and SVD with compressive sampling framework

Novamizanti, Ledya; Budiman, Gelar; Astuti, Elsa Nur Fitri

doi:10.12928/telkomnika.v18i2.14773

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…DCT has many performed because of its optimal accomplishment, it applied in the signal, image analysis, and applied especially in speech compression because of its optimal achievement. DCT transforms an input signal from the time domain to the frequency domain and its one-dimensional form is good for the examination of one-dimensional signals like speech signals [21,22]. DCT is composed of (DC) and (AC) coefficients, where the first coefficient C (0) is named the DC coefficient and carries average signal value and the rest coefficients are indicated as the AC coefficients [23].…”

Section: Discrete Cosine Transform (Dct)mentioning

confidence: 99%

Audio compression using transforms and high order entropy encoding

Ahmed

George

Hadi

2021

IJECE

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<span>Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity.</span>

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Section: Discrete Cosine Transform (Dct)mentioning

confidence: 99%

Audio compression using transforms and high order entropy encoding

Ahmed

George

Hadi

2021

IJECE

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“…Image's algebraic characteristics might be specified, also SVD has been majorly utilized in the image processing. Due to its rotation invariance and stability, the majority of present algorithms of image encryption have been on the basis of SVD that have elevated robustness [9][10][11]. An excellent approach for computing eigenvectors and eigenvalues of data matrix X (KxM) has been with the use of SVD specified as follows [12]…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

“…SNR over 40 dB offers optimum quality of the image which is close to original image; SNR with 30-40 dB generally producing excellent quality of the image with adequate distortions; SNR with 20-30 dB presenting bad quality of the image; SNR not more than 20 dB generating undesirable image [26]. Furthermore, the calculation approaches for NMSE and PSNR [27] have been provided in the following way: (11) In which MSE representing MSE between original image ( ) as well as denoised image (̂) with size M×N:…”

Section: Performance Measuresmentioning

confidence: 99%

Improved anti-noise attack ability of image encryption algorithm using de-noising technique

Abdulwahed

Ahmed

2020

TELKOMNIKA

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Information security is considered as one of the important issues in the information age used to preserve the secret information throughout transmissions in practical applications. With regard to image encryption, a lot of schemes related to information security were applied. Such approaches might be categorized into 2 domains; domain frequency and domain spatial. The presented work develops an encryption technique on the basis of conventional watermarking system with the use of singular value decomposition (SVD), discrete cosine transform (DCT), and discrete wavelet transform (DWT) together, the suggested DWT-DCT-SVD method has high robustness in comparison to the other conventional approaches and enhanced approach for having high robustness against Gaussian noise attacks with using denoising approach according to DWT. Mean square error (MSE) in addition to the peak signal-to-noise ratio (PSNR) specified the performance measures which are the base of this study's results, as they are showing that the algorithm utilized in this study has high robustness against Gaussian noise attacks.

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“…It can only be applied on certain signals and the entire framework-sampling and reconstruction-has to be tailored to each individual application. Despite this disadvantage CS has found its way into applications such as medical imaging [5][6][7][8], audio [9] and video [10][11][12] processing, vibration sensing [13,14] data gathering [15] etc.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing with continuous parametric reconstruction

Andráš

Michaeli

Šaliga

2021

IJECE

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This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

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Robust audio watermarking based on transform domain and SVD with compressive sampling framework

Cited by 10 publications

References 19 publications

Audio compression using transforms and high order entropy encoding

Audio compression using transforms and high order entropy encoding

Improved anti-noise attack ability of image encryption algorithm using de-noising technique

Compressed sensing with continuous parametric reconstruction

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