Digital audio watermarking is adopted for many multimedia applications such as copyright management, music indexing, broadcast monitoring etc. Audio watermarking hides copyright information into the digital audio signal. Simple audio watermarking provides copyright information but authentication/security of the audio can be achieved by using a chaotic sequence in the watermarking process. For which Discrete Wavelet Transform (DWT) along with Arnold Transform is proposed to describe audio watermarking and its authentication. Subjective and objective tests unveil that the proposed watermarking scheme preserves high quality and is simultaneously more robust to different attacks. Performance of the algorithm has been compared with the Discrete Cosine Transform (DCT) and Arnold transform based method.

Digital Audio Watermarking involves the concealment of data within a discrete audio file. Intellectual property protection is the main driving force behind research in this area. So, an efficient yet simple audio watermarking algorithm in the frequency domain by embedding a visual watermark in the audio signal is proposed. Discrete Cosine Transform (DCT) along with Singular Value Decomposition (SVD) is used to embed the watermark image into the audio signal. Similarly, Discrete Wavelet Transform (DWT) and SVD is used as another technique. The performance of the two techniques is evaluated based on PSNR, MOS criteria and Pearson Correlation Coefficient (PCC). Experimental results show that the DCT-SVD method has good imperceptibility and is robust against different kinds of attacks such as noise adding, re-sampling, cropping.

Watermarking is the process of embedding information into a signal (e.g. audio, video or pictures) in a way that is difficult to remove. In this paper, a high-capacity audio watermarking system is used to embed data and extract them using singular value decomposition (SVD). With the help of SVD-based algorithms and by using lifting wavelet transform (LWT), discrete cosine transform (DCT) and discrete wavelet transform (DWT). DCT-SVD, DWT-SVD, DWT-DCT-SVD, LWT-DCT-SVD techniques are developed. It was observed that by increasing the quantization levels the signal-to-noise ratio (SNR) value decreases exponentially which leads to distortion in the original signal. It is also observed that robustness is also increased by applying different malicious attacks like re-sampling, re-quantization, echo addition, cropping, additive white gaussian noise (AWGN), signal addition and signal subtraction to the embedded signal which doesn't disturb the original signal and the extracted image. The performance of this technique is evaluated using bit error rate (BER), cross-correlation (CC) and SNR.