This chapter introduces an LWT-based audio watermarking scheme using fast Walsh-Hadamard transform (FWHT) and singular value decomposition (SVD) [30]. Conventional wavelet transform provides good results for its multi-resolution characteristics and perfect reconstruction. However, it is mainly calculated by convolution operation, resulting in high computation. In addition, the generated floating numbers increase the storage requirements. As a result, the LWT is designed to increase the efficiency and it is now used in digital watermarking [27]. In our proposed method, watermark information is preprocessed first using a Bernoulli map in order to improve the robustness and enhance the confidentiality of the watermark. Then the original audio is segmented into nonoverlapping frames. Watermark information is embedded into the largest singular value of the FWHT coefficients obtained from the low-frequency LWT coefficients of each frame. A blind watermark detection technique is developed to identify the embedded watermark under various attacks. The main features of the proposed scheme are: (i) it utilizes the LWT, FWHT, and SVD jointly; (ii) it uses Bernoulli map, containing the chaotic characteristic to enhance the confidentiality of the proposed scheme; (iii) watermark extraction process is blind; (iv) subjective and objective evaluations reveal that the proposed scheme maintains high audio quality; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Experimental results indicate that the proposed watermarking scheme is highly robust against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms state-of-the-art methods [9–10, 14–16, 20, 23, 26, 28] in terms of imperceptibility, robustness, and data payload. The data payload of the proposed scheme is 172.39 bps, which is relatively higher than that of the state-of-the-art methods.