## 1 Introduction  
With the rapid development of the Internet, multimedia data stored in digital form can be easily replicated and destroyed by illegal users, so protection against intellectual property infringement increasingly becomes an important issue. There are two primary methods to overcome the above problems, which are digital signature [1,2] and digital watermarking [3,4]. Digital signature is a kind of number string which can be used as the secret key for both senders and receivers [5], and it easily stimulates the desire of illegal users to destroy the multimedia data. Digital watermarking technology conceals the watermarks into the multimedia data and later extracts such watermarks to prove the owner of multimedia data, so it is an efficient approach to protect the media contents, widely used for copyright protection, broadcast monitoring, fingerprinting, data authentication, and medical safety, and it has become a hot topic in the field of communication and information security in recent years [6; 7; 8]. According to the different application carriers, digital watermarking technology can be divided into image watermarking technology [9; 10], video watermark technology [11], audio watermark technology, and so on. Compared with the image watermarking technology, it is harder to develop the audio watermarking technology, mainly because the human auditory system is more sensitive than the visual system. With the widespread presence of audio media on the Internet, more and more people are beginning to pay attention to the research about audio watermarking technology, and more and more research algorithms have appeared. An audio watermarking algorithm generally takes into consideration four aspects including imperceptibility, robustness, security and payload capacity [12]. These four indexes are in conflict with each other. The increase of one index may cause a decrease in other indexes. An excellent audio watermarking algorithm should not only guarantee that the watermarked audio has good imperceptibility, but also provide enough payload capacity to accommodate necessary information. In addition, it should have a strong robustness to resist various signal processing attacks in practical application, so as to ensure the security of the extracted watermark. At present, most audio watermarking algorithms have some shortcomings, such as low capacity, poor robustness, and serious decline of the carrier audio quality. The audio watermarking algorithm for copyright protection must have good imperceptibility and strong robustness in resisting most common signal processing attacks that the audio may suffer during the transmission process to ensure that the watermark can be extracted accurately. This paper presents an audio watermarking algorithm in the discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains. The multi-resolution analysis characteristic of DWT renders excellent robustness against most attacks, and DCT has the merits of high energy compaction in low-frequency coefficients, which makes it suitable for compression applications, so these two methods are widely used in signal processing [13; 14; 15]. In this study, our aim is to explore all useful properties of the DWT and DCT for audio watermarking so that the issues of robustness, imperceptibility, security and payload capacity can be improved as much as possible. In this algorithm, the carrier audio signal is first segmented into multiple audio fragments (the number of fragments should be larger than the length of watermark), and then each audio fragment is followed by DWT and DCT in order to obtain the two sets of TDC which are modified to embed the binary watermark. The major contributions of this algorithm are as follows. Firstly, a novel audio watermarking algorithm based on DWT and DCT is presented by modifying the average amplitudes of the transform coefficients to embed the watermark.