Advances in Audio Watermarking Based on Singular Value - download pdf or read online

By Pranab Kumar Dhar, Tetsuya Shimamura

ISBN-10: 3319147994

ISBN-13: 9783319147994

ISBN-10: 3319148001

ISBN-13: 9783319148007

This booklet introduces audio watermarking equipment for copyright safety, which has drawn vast consciousness for securing electronic information from unauthorized copying. The booklet is split into elements. First, an audio watermarking approach in discrete wavelet remodel (DWT) and discrete cosine rework (DCT) domain names utilizing singular price decomposition (SVD) and quantization is brought. this system is strong opposed to quite a few assaults and offers stable imperceptible watermarked sounds. Then, an audio watermarking process in speedy Fourier rework (FFT) area utilizing SVD and Cartesian-polar transformation (CPT) is gifted. this technique has excessive imperceptibility and excessive information payload and it offers solid robustness opposed to numerous assaults. those thoughts let media vendors to guard copyright and to teach authenticity and possession in their fabric in a number of functions.

· good points new equipment of audio watermarking for copyright safeguard and possession protection

· Outlines thoughts that offer more desirable functionality when it comes to imperceptibility, robustness, and information payload

· comprises purposes corresponding to info authentication, info indexing, broadcast tracking, fingerprinting, etc.

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Additional resources for Advances in Audio Watermarking Based on Singular Value Decomposition

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Step 9: The inverse DCT is performed on the modified matrix Yi0 to obtain the matrix Hi0 which contains the modified detailed coefficients D10 and D20 . Step 10: After substituting the coefficients D10 and D20 for D1 and D2 , respectively, a two-level inverse DWT is performed to obtain the watermarked audio frame. Step 11: Finally, all watermarked frames are concatenated to calculate the watermarked audio signal. 2 Watermark Detection Process The proposed watermark detection process is shown in Fig.

4. Requantization: The 16-bit watermarked audio signal is quantized down to 8 bits/sample and again requantized back to 16 bits/sample. 5. MP3 compression: MPEG-1 layer 3 compression is applied. The watermarked audio signal is compressed at a bit rate of 128 kbps and then decompressed back to the wave format. 0 was used in our experiment for resampling, requantization, and MP3 compression attacks. 8. 11 shows the extracted watermarks along with the NC and BER values after several different attacks for the audio signal ‘Classical’.

We observed that the variation of the largest singular value modifies all DCT coefficients slightly, which can provide high robustness against different attacks as well as goodquality watermarked sounds. 1 Imperceptibility Test The imperceptibility of the watermarked audio signal is evaluated using a subjective listening test and an objective test. 1 0 50 DWT domain 50 100 Fig. 2 0 50 100 150 200 DWT Domain Fig. 2 0 50 100 150 DCT domain 200 250 Fig. 2 0 50 100 150 DCT domain 200 Fig. 1 SDG 5 4 3 2 1 ODG 0 1 2 3 4 Description Imperceptible Perceptible, but not annoying Slightly annoying Annoying Very annoying Quality Excellent Good Fair Poor Bad Subjective Listening Test The subjective difference grade (SDG) is one of the most widely used subjective methods for evaluating the quality of a watermarked audio signal.

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


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