近幾年，隨著多媒體技術的進步及通訊網路的蓬勃發展，為了有效率的儲存與傳遞多媒體的音頻資訊，需要對這些音頻訊號做適當的壓縮處理。現今大部分音訊編碼系統乃是利用心理聲學模型來估算人耳對聲音的遮蔽效應，使得訊號編碼的資料量得以降低，但一般的心理聲學模型是以快速傅立葉轉換(Fast Fourier Transform, FFT)來進行分析，所以需要大量的數值運算量。
　　而依不同的多媒體應用特性，如傳輸頻寬、傳輸即時性等有著不同的網路需求，為了使多媒體應用達到即時、有效率傳輸的目的，本研究提出一低複雜度的可調式音訊編碼系統，其原理乃是利用離散小波封包分頻方式來模擬符合人耳聽覺響應中的非線性關鍵頻帶，不但可以改善非穩態聲音分析，也因在小波域上計算其遮罩曲線，所以能有效地減少心理聲學模型的運算量。本研究的音訊編碼系統利用零樹編碼的技術來建立一個三層可調性編碼，使接收端能針對不同頻寬選擇其適當的位元率來傳輸。不同於傳統零樹編碼演算法，本研究還配合心理聲學模式將聽不見的訊號移除，使得在低位元率時仍有不錯的音質。實驗結果指出與MPEG-1 Layer I和傳統零樹編碼的演算法比較，我們提出的方法都有較佳的音質與較低的運算複雜度。

　In recent years, multimedia technique and communication has been developed rapidly. The multimedia data in order to be effectively stored and transmitted by variable channel (e.g. Internet), need proper to do compression and processing the multimedia signal especially. In order to reduce audio encode data, most of modern audio codecs exploits the psychoacoustic model to remove audio irrelevancy without losing audio quality. However, the Fast Fourier Transform (FFT) which has been used by psychoacoustic models to analyze the frequency components requires high computational complexity.
　Scalable coding is desirable for heterogeneous network with varies bandwidths. In this research, we propose a low complexity scalable audio coding system. The wavelet packet decomposition was utilized in a way to closely mimic the nonlinear critical band. Since our audio coder directly calculates the masking thresholds in wavelet domain, our audio coder can not only improve on analysis of nonstationary audio signal but also the complexity is greatly reduced. We build a three-layer scalable audio coding system based on a zero-tree coding algorithm, thus the decoder can choose its suitable bandwidth to transmit the data. The main different from our algorithm and a traditional zero-tree coding algorithm is that we have taken input from the psychoacoustic model so that the inaudible audio signal would be removed and furthermore the audio still remains its quality at the low bit rate. The results indicate that we proposed algorithm compares with MP-1 and traditional zero-tree algorithm has the better quality and it is apparent that our audio coder required less the computational effort than general psychoacoustic model.

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