Advances in Audio Watermarking Based on Singular Value by Pranab Kumar Dhar, Tetsuya Shimamura

By Pranab Kumar Dhar, Tetsuya Shimamura

This e-book introduces audio watermarking tools for copyright safeguard, which has drawn vast consciousness for securing electronic information from unauthorized copying. The booklet is split into elements. First, an audio watermarking procedure in discrete wavelet rework (DWT) and discrete cosine remodel (DCT) domain names utilizing singular worth decomposition (SVD) and quantization is brought. this technique is strong opposed to quite a few assaults and offers reliable imperceptible watermarked sounds. Then, an audio watermarking approach in quick Fourier rework (FFT) area utilizing SVD and Cartesian-polar transformation (CPT) is gifted. this system has excessive imperceptibility and excessive information payload and it presents reliable robustness opposed to numerous assaults. those recommendations permit media vendors to guard copyright and to teach authenticity and possession in their fabric in various functions.

· positive factors new equipment of audio watermarking for copyright security and possession protection

· Outlines innovations that offer more desirable functionality by way of imperceptibility, robustness, and information payload

· comprises purposes similar to facts authentication, information indexing, broadcast tracking, fingerprinting, etc.

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Extra info for Advances in Audio Watermarking Based on Singular Value Decomposition

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3 Error Analysis We analyzed the performance of the proposed method in terms of the false positive error (FPE) and false negative error (FNE). It is difficult to give an exact probabilistic model for an FPE and an FNE. 3203 based on a binomial probability distribution similar to that in [8] to estimate the probability of an FPE and an FNE for our proposed method. 1 False Positive Error An FPE occurs when an unwatermarked audio signal is declared as a watermarked audio signal by the detector. Let k be the total number of watermark bits and h be the total number of matching bits.

The performance of our method is assessed in terms of imperceptibility, robustness, and data payload. 4 Experimental Results and Discussion 23 Fig. 1 kHz were used. 94 s). By using a frame size of 256 samples, we have 1,024 nonoverlapping frames for each audio signal. In each frame of the audio signal, we embed 1-bit binary watermark information. The embedded watermark is a binary logo image of size M M D 32 32 D 1;024 bits, as shown in Fig. 4. 5. For convenience, the selected values for constants C1 and C2 are both 8.

Step 6: Finally, the binary watermark image WBI is obtained by rearranging the watermark sequence into a square matrix of size M M . 8 -1 DCT and square matrix generation SVD 0 1 2 3 4 5 6 Pre-embedding process Pre-embedding process Watermark extraction Extracted watermark image Fig. 4 Experimental Results and Discussion In this section, we report several experiments carried out to demonstrate the performance of the proposed watermarking method. The performance of our method is assessed in terms of imperceptibility, robustness, and data payload.

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