本論文以小波封包 (Wavelet Packet) 的分頻方式，將樂音訊號經由濾波器群組分成29個次頻帶，藉由人耳聲學模型 (Psychoacoustic Model) 找出適合嵌入的頻段，之後使用小波量化調變的方法進行嵌入的動作，此方法並無傳統做法中對原始音訊的需求，可以省去原始音訊的儲存空間。
針對以音框為基礎(Frame-based)之嵌入架構，如果每個音框皆使用不同的步階大小，將會造成龐大的資訊藏量。為了滿足最低可接受之音訊品質，由統計結果發現小波係數的L1 norm與步階大小成線性關係，故我們提出調適性的步階來符合不同樂音的需求。此步階大小可同時由嵌入端及萃取端計算而得，如此則不需額外的儲存空間來記錄步階大小。
本音訊浮水印技術可以提供平均每個音框大約4個位元的浮水印容量，由主觀的測試中知道，嵌入後之主訊號音質與原音質相近。由客觀測試知道，嵌入浮水印之樂音和原始樂音的訊雜比維持在20dB以上。且根據實驗結果顯示經一般的數位訊號處理，或欲藉由對樂音訊號的攻擊，如MP3音訊壓縮64K bps以上、重新取樣、對取樣點重新量化、加入雜訊等處理，正規化相關性(Normalized Correlation)值皆在0.8以上，達到均可辨識之程度，可見其強健性。

In this paper, we propose a robust audio watermarking technique which adopts the wavelet QIM method with adaptive step sizes for blind watermark extraction. Since wavelet transform offers both temporal and frequency resolutions, it is suitable for audio signal processing. The original audio signal is first segmented and divided into 29 subbands via wavelet packet decomposition. The bandwidth allocation of the subband decomposition structure is close to the critical band structure of human auditory system. According to Psychoacoustic Model, middle-low subbands are chosen for watermark embedding.
The adaptive step size technique is applied to audio signals with different characteristics based on the criterion that SNR must be maintained above 20 dB so that it is robust and transparent. We formulate the step size of each frame to be proportional to the magnitude of the audio signal. No side information on the step sizes need to be transmitted.
We analyze the performance of the proposed algorithms in terms of data SNR and Normalized Correlation (NC). The experimental results show that the embedding capacity is around 4 bits/frame and the watermark is robust against MP3 compression at 64 Kbps, resampling, requantization, and Gaussian noise corruption. The NC values after attacks are all above 0.8 in the experiments so that the copyright can easily be distinguished.

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