论文标题
噪音环境中语音识别系统的声学回声取消算法的比较研究
Comparative Study of Acoustic Echo Cancellation Algorithms for Speech Recognition System in Noisy Environment
论文作者
论文摘要
传统上，通过使用诸如归一化均值最小平方（NLMS）算法等算法估算声学回声响应来估算声学回声响应来实现AEC。近年来，已经提出了几种方法，以通过各种方式提高标准NLMS算法的性能。 These include algorithms based on Time Domain, Frequency Domain, Fourier Transform, Wavelet Transform Adaptive Schemes, Proportionate Schemes, Proportionate Adaptive Filters, Combination Schemes, Block Based Combination, Sub band Adaptive Filtering, Uniform Over Sampled DFT Filter Banks, Sub band Over-Sampled DFT Filter Banks, Volterra Filters, Variable Step-Size (VSS) algorithms, Data Reusing技术，部分更新自适应过滤技术和子频段（SAF）方案。这些方法旨在解决回声取消的问题，包括具有嘈杂的输入信号，随时间变化的回声路径和计算复杂性的性能。与这些方法相反，已经开发了稀疏的自适应算法，以解决稀疏系统识别中自适应过滤器的性能。在本文中，我们讨论了一些AEC算法，然后进行了比较研究，分别与阶梯，收敛性和性能。
Traditionally, adaptive filters have been deployed to achieve AEC by estimating the acoustic echo response using algorithms such as the Normalized Least-Mean-Square (NLMS) algorithm. Several approaches have been proposed over recent years to improve the performance of the standard NLMS algorithm in various ways for AEC. These include algorithms based on Time Domain, Frequency Domain, Fourier Transform, Wavelet Transform Adaptive Schemes, Proportionate Schemes, Proportionate Adaptive Filters, Combination Schemes, Block Based Combination, Sub band Adaptive Filtering, Uniform Over Sampled DFT Filter Banks, Sub band Over-Sampled DFT Filter Banks, Volterra Filters, Variable Step-Size (VSS) algorithms, Data Reusing Techniques, Partial Update Adaptive Filtering Techniques and Sub band (SAF) Schemes. These approaches aim to address issues in echo cancellation including the performance with noisy input signals, Time-Varying echo paths and computational complexity. In contrast to these approaches, Sparse Adaptive algorithms have been developed specifically to address the performance of adaptive filters in sparse system identification. In this paper we have discussed some AEC algorithms followed by comparative study with respective to step-size, convergence and performance.
