應用盲點來源分離演算法分解數位助聽器中的混合語音訊號

由於傳統的助聽器基本原理是將外界的聲音經由麥克風接收後，接著由擴大機來將聲音的振幅放大，但麥克風會同時接收到語音訊號和環境噪音，因此不但放大了語音訊號，連帶的也將環境中的噪音一起放大。許多的研究學者也提出了各種的噪音消除演算法用於消除某些特定頻率的噪音，但這些噪音消除演算法並無法有效的降低複雜且多變的環境噪音。隨著科技不斷的日益增進，盲訊號分離技術有機會可以成為消除噪音的另一項選擇，其中獨立成份分析法為盲訊號分離的主要方法之一，其是由類神經網路和統計學理論相結合而產生的方法。而本文也將經由三種不同之獨立成份分析法：快速獨立成份分析法、資訊最大獨立成份分析法、特徵矩陣聯合近似對角化法，做一系列的模擬，重點在測試獨立成份分析法在分離混合語音訊號上的效能。但是由於獨立成份分析法並不適用於分離迴旋的混合的聲音訊號，因此本文必須在某些限制的條件下去做模擬，目的是使獨立成份分析法能維持一定的分離效能。根據本文模擬結果顯示，語音訊號經由獨立成份分析法重建後，其內容幾乎與原始語音內容相同，而且此演算法對訊號成份破壞也較少，因此也較能保留訊號本身的特性。所以應用於分解數位助聽器中的混合語音是個不錯的選擇。

關鍵字

獨立成份分析；盲點來源分離；資訊最大獨立成份分析；快速獨立成份分析；聯合對角化演算法

並列摘要

As result of receiving outside sounds by microphones, and then amplitude of the sounds amplification by amplifier, the fundamental principles of the traditional hearing aid recorded sounds that mixed signals including speech signals and noises; therefore they amplitude not only the sounds, but also the background noises. Many researchers proposed various algorithms for eliminating some of the specific noises frequency. However, those algorithms usually can’t reduce the sophisticated and changeable noises effectively. With the incessant improved development of the technology, the Blind Source Separation (BSS) elevated chance to eliminate noises. Among BSS, the Independent Component Analysis (ICA) is the dominant methods, which combined by Neural network theory and statistics. In this thesis, we performed a series of simulation based on three different kinds of algorithms, those are Information-MaximizationICA(InfomaxICA), FastICA and Joint Approximate Diagonalization of Eigenmatrices (JADE), and also focus on the separation performance of ICA for separating mixed speech signals. Nevertheless, because of the conventional ICA is not well suit for separating convolutive acoustic signals, therefore we conducted simulations under some special conditions to enable ICA to maintain a certain degree of separating performance. According to our simulation results, after speech signals separated by ICA, we can find the contents of the recovered signals are the same with the original ones, and owing to less destruction of the signals so can the characteristics of original retain more. Therefore, it is a good choice to apply on separating mixed speech signals in digital hearing aids.

並列關鍵字

Independent Component Analysis ； Blind Source Separation ； Information- Maximization ICA ； Fast ICA ； Joint Approximate Diagonalization of Eigenmatrices

參考文獻

[3] J. V. Stone, “Independent components analysis: an introduction,” Trends in Cognitive Sciences, Vol.6(2), pp.59-64, February 2002.

[4] B. Widrow, and F. Luo, “Microphone arrays for hearing aids: an overview,” Speech Communication, Vol.39, pp.139-146, 2003.

[5] T. W. Tillman, R . Carhart, and W. O. Olsen, “Hearing aid efficiency in a competing speech situation,” Journal of Speech and Hearing Research, Vol.13, pp.789-811, December 1970.

[6] S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Transactions on Acoustics, Speech, Signal Process, Vol.27(1), pp.113–120, 1979.

[7] N. Virag, “Single channel speech enhancement based on masking properties of the human auditory system,” IEEE Transactions on Acoustics, Speech, Signal Process, Vol.7(2), pp.126–137, 1999.

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應用盲點來源分離演算法分解數位助聽器中的混合語音訊號

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