Efficient Blind Dereverberation and Echo Cancellation Based on Independent Component Analysis for Actual Acoustic Signals

Abstract

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT₂₀= 670 ms reverberant environment.

Weighted Recursive Least Square Filter and Neural Network Based Residual ECHO Suppression for the AEC-Challenge

“…Addressing AEC from the source separation perspective has been investigated in [19,20], and ICA based solutions are discussed therein. Here, an Aux-ICA based solution is derived and results in a novel weighted RLS filter.…”

A comparative study of echo cancellation algorithms based on blind source separation

“…Wada 等 [15][16][17] 深 入分析了基于半盲源分离数据批处理的立体声回声 抵消滤波器的收敛性能、回声 返回损失增益 (Echo Return Loss Enhancement, ERLE)、失调表现和算法稳 定性. Takeda 等 [18] 提出了一种结合了去混响和回声消 除的低复杂度算法. Gunther 等 [19,20] 利用多维独立成分 分析(Multidimensional ICA, MICA)的思想, 通过计算 非平稳声源的时域相关函数实现单通道线路回声和 声学回声消除, Koldovský 等 [21] 在此基础上将其改进 为在线自适应算法.…”