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首页> 外文会议>National Conference on Communications >Estimation of allpass transfer functions by introducing sparsity constraints to particle swarm optimization
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Estimation of allpass transfer functions by introducing sparsity constraints to particle swarm optimization

机译：通过将稀疏约束引入粒子群优化来估计全通传递函数

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An algorithm to estimate allpass transfer functions by assuming sparsity over the input signals is proposed in this paper. As a tractable measure of sparsity, the l1 norm of input signal is minimized and the set of allpass coefficients which realizes the l1 norm minimization is obtained. It is observed that the estimation of allpass systems with sparse inputs is a nonconvex problem and hence a nonconvex optimization method-the particle swarm optimization (PSO) is used. With PSO, a large number of uniformly chosen points in a d-dimensional problem space are guided towards an optimum solution with respect to the l1 norm of input signal. Experimental results show that PSO is successful in estimating allpass transfer functions. Application of allpass filter estimation to speech processing is also studied and results which portray the effectiveness of the proposed method are reported.
机译：提出了一种通过假设输入信号的稀疏性来估计全通传递函数的算法。作为稀疏性的一种可衡量的度量，将输入信号的l1范数最小化，并获得实现l1范数最小化的全通系数集。可以看出，具有稀疏输入的全通系统的估计是一个非凸问题，因此使用了非凸优化方法-粒子群优化（PSO）。使用PSO，可以将d维问题空间中的大量均匀选择的点引导到关于输入信号的l1范数的最佳解决方案。实验结果表明，PSO成功地估计了全通传递函数。还研究了全通滤波器估计在语音处理中的应用，并报道了证明该方法有效性的结果。

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