在智慧型感知系統中，觀察訊號混合了許多未知的獨立訊號，因此，如何將未知獨立訊號有效分離出來是機器學習領域中的重要課題，而獨立成份分析(independent component analysis, ICA)是被廣泛應用於未知訊號分離(blind source separation, BSS)的問題上。在標準獨立成份分析中，假設所有的未知獨立訊號都是處於穩定的程序(Stationary Process)，然而，未知訊號可能會動態的改變位置或者處於啟動(active)以及未啟動(inactive)的狀態，因此標準的獨立成份分析在實際應用上是受到限制。為了在智慧型感知系統中提供更有效的訊號分離的效果，本研究擬提出非穩定性貝氏獨立成份分析演算法，透過線上貝氏學習機制，達到即時估測非穩定性未知訊號的啟動狀態與個數，並且改進文獻中相關演算法的計算複雜度。再者，在智慧型感知系統中，除了要能正確分離混合訊號之外，相關的應用，例如：語者認證、語音辨識及以語者分割與分群系統等應用上，如何將訊號斷點有效偵測出來是必要的。因此，本研究透過半馬可夫模型(semi-Markov model)來描述非穩定性貝氏獨立成份分析模型參數在區間長度(duration)上的資訊，進而估測出正確的非穩定未知訊號的斷點資訊。最後，我們模擬各種非穩定性的訊號混合並評估本論文提出的非穩定性貝氏獨立成份分析演算法的解混合效果，驗證本演算法在非穩定性未知訊號分離的優越性，在初步的實驗結果中我們提出的方法比文獻最新的方法達到較高的訊號干擾比(signal-to-inference ratio)，並且有較好的斷點偵測效果。

In an intelligent speech perception system, it is required to recover speech signals from the mixed signals where some unknown and independent sources are simultaneously acquired by the system microphones. As we known, the independent component analysis (ICA) is a popular approach for blind source separation (BSS) and is referred as an important issue in the fields of machine learning. Traditionally, the standard ICA assumes that the source signals are stationary. This assumption restricts the performance of ICA in real-world applications. Since the source signals may be moving or may be active or inactive as time goes on, we propose a nonstationary Bayesian ICA (NB-ICA) for dealing with the nonstationary blind source separation for an intelligent speech perception system. The proposed NB-ICA algorithm is based on the online Bayesian learning theory which identifies the source activity and estimates the number of source signals in real time. Also, comparing with the other nonstationary ICA algorithms, the computation cost of the proposed NB-ICA can be significantly reduced. In addition, the segmentation information of the source signals is important for the applications of intelligent speech perception such as the systems of speech recognition, speaker identification and speaker diarization. Accordingly, we incorporate a semi-Markov model for capturing the duration information for the estimated status of the source signals. The experimental results show that the proposed NB-ICA algorithm is efficient for the separation of nonstationary source signals in terms of signal-to-inference ratio and detection accuracy of signal segments.

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