本論文結合麥克風陣列與喇叭陣列的技術，建立一套能應用於語音助理上的聲學陣列系統。在麥克風陣列系統，多重信號分類法(Multiple signal classification, MUSIC)的方法被用來來估算平面波的方向。在估算階段本論文提出利用修正型粒子群最佳化法結合適應性參數(Modified particle swarm optimizer with adaptive coefficients, MPSO-AC)演算法去更有效率的搜索多聲源的位置。本論文亦提出使用提克諾夫正規化結合閥值技術(Tikhonov Regularization with thresholding ,TIKR-thresholding)方法來擷取聲源訊號。定位的結果指出MPSO-AC搜索相較於傳統等間距網格式搜索在運算量有大幅度的減少，並且能達到更高的定位精準度。信號提取的部分，在多聲源的環境下實驗結果顯示TIKR-thresholding的方法能夠在多聲源的環境下顯著地增強信號的品質。在喇叭陣列系統中，以時域欠定多通道逆預濾波器（time-domain underdetermined multichannel inverse pre-filters, TUMIF）的雙耳音訊呈現系統為基礎，並將此系統應用在串音消除(cross-talk cancellation system, CCS )、聲源拓寬(source widening)和五通道虛擬環繞音效(5-channel virtual surround audio)等三種技術上。在設計逆濾波器時採用了TIKR 演算法來限制逆濾波器的增益值。客觀和主觀的測試結果證明了TUMIF方法對於雙耳音效的有效性。

This thesis combines the microphone array and the loudspeaker array technology to establish an acoustic array system that can be applied to voice assistant. In microphone array system, Multiple Signal Classification (MUSIC) is used to estimate the Direction of Arrival (DOA) of sources. In addition, the Modified Particle Swarm Optimizer algorithm with adaptive coefficients (MPSO-AC) is used to locate the multiple source efficiently. This thesis also applies Tikhonov Regularization with Thresholding (TIKR-thresholding) to extract source signals. The results of source localization show that the MPSO-AC search effectively reduces the computational load and achieve higher localization accuracy than the uniform grid search, and MPSO with fixed coefficients. The separation results also show that the quality of source signals extracted by using the TIKR-thresholding method is significantly enhanced in multi-sources scenarios. The loudspeaker array system is based on binaural audio rendering system designed with the time-domain underdetermined multichannel inverse pre-filters (TUMIF). The system is applied to cross-talk cancellation system (CCS), source widening, and 5-channel virtual surround audio. The TIKR algorithm is used to limit the gain of the inverse filters. Results of objective and subjective tests have demonstrated the efficacy of the TUMIF approach for binaural audio rendering.

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