By Pranab Kumar Dhar, Tetsuya Shimamura
This booklet introduces audio watermarking equipment for copyright safety, which has drawn vast consciousness for securing electronic info from unauthorized copying. The ebook is split into elements. First, an audio watermarking strategy in discrete wavelet remodel (DWT) and discrete cosine remodel (DCT) domain names utilizing singular worth decomposition (SVD) and quantization is brought. this technique is strong opposed to a number of assaults and offers reliable imperceptible watermarked sounds. Then, an audio watermarking technique 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 offers stable robustness opposed to a number of assaults. those suggestions let media vendors to guard copyright and to teach authenticity and possession in their fabric in quite a few purposes.
· positive aspects new equipment of audio watermarking for copyright safety and possession protection
· Outlines thoughts that supply better functionality by way of imperceptibility, robustness, and knowledge payload
· comprises functions akin to information authentication, info indexing, broadcast tracking, fingerprinting, etc.
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Extra resources for Advances in Audio Watermarking Based on Singular Value Decomposition
39 bps. 5, which is based on the reported results in [8, 28, 29, 33, 62, 65, 66]. 5. From the comparison of results, it is seen that the proposed method achieves a higher data payload and a lower BER against several attacks than the state-of-the-art methods. Overall, our proposed method provides superior performance to the state-of-theart audio watermarking methods while maintaining a very good trade-off among the conflicting requirements of imperceptibility, robustness, and data payload. 5 Summary In this chapter, at first a brief discussion on SVD-based watermarking methods was presented.
1 Introduction This chapter presents a DWT-DCT-based audio watermarking method using SVD and quantization [22, 23]. In the proposed method, initially the original audio signal is segmented into non-overlapping frames. DWT is applied to each frame and detail coefficients are represented in matrix form. DCT is performed on the detail coefficients and the obtained DCT coefficients are reshaped. SVD is applied to the reshaped DCT coefficients of each frame. Watermark information is then embedded into the highest singular value of each audio frame by quantization.
SVD is applied to the selected FFT coefficients of each frame represented in a matrix form. The highest two singular values of each frame are selected. The selected singular values are assumed as the components of polar coordinate system and are transformed into the components of Cartesian coordinate system. Watermark information is embedded into each of these Cartesian components using an embedding function. This is because the low frequency FFT coefficients correspond to the energy of the most perceptually significant regions in an audio signal and slight variations of the Cartesian components of the largest singular values do not significantly affect the quality of the signal.