【摘要】 针对独立矢量分析(IVA)算法初始分离矩阵取值对分离性能影响较大的局限性,提出了基于回溯搜索优化的卷积混合语音盲分离算法。采用频域各频率点IVA分离信号的复数峭度和作为目标函数,利用回溯搜索优化算法(BSA)对初始分离矩阵进行优化调整,更好地实现了语音信号的盲分离。在分离过程中,采用复Givens旋转变换原理将对分离矩阵的求解转化为对旋转角度的求解,有效减少了BSA的参数编码维数,降低了优化求解难度。针对语音信号的卷积混合分离实验表明,该算法具有良好的分离效果,其分离性能较之基本IVA算法显著提升。更多还原

【Abstract】 Aiming to overcome the limitation of initial separation matrix selection in Independent Vector Analysis(IVA),a convolutive blind speech separation algorithm based on backtracking search optimization is proposed. The sum of complex kurtosis of separated signals in each frequency point from IVA is used as the objective function. The Backtracking Search Optimization Algorithm(BSA)is used to adjust the initial separation matrix for better separation. In the separation process, complex Givens rotation transformation is used to transform separation matrix to rotation angle for reducing the coding dimension of BSA and the difficulty of optimization decreases. The blind speech separation experiments for convolutive mixture signals indicate that the proposed algorithm performs excellent separation results and the separation property is better than the basic IVA algorithm.更多还原