Wavelet based ICA using maximisation of non-Gaussianity for acoustic echo cancellation during double talk situation

A Fetal Electrocardiogram Signal Extraction Algorithm Based on the Temporal Structure and the Non-Gaussianity

“…BSS algorithms can separate all sources from mixtures without any priori knowledge. In BSS algorithms, the ICA model [ 7 – 9 ] utilizes the non-Gaussianity [ 10 ] of signals to separate all source signals. This model is suitable for biomedical signal processing [ 11 ] and non-Gaussianity becomes an important tool to process these kinds of signals.…”

Independent Component Analysis Applied to Hermetic Reciprocating Compressors to Predict Noise Sources

“…Among the various BSS techniques, independent component analysis (ICA) aims to recover sources in a system based on the statistical independence between them [16] and is presented as a tool for hierarchizing and classifying sources. One can cite the success of ICA in several knowledge areas: machine fault diagnosis [17], vibration and noise monitoring and control [18], modal structure parameters [19], fault feature extraction for rolling element bearings [20], echo cancellation [21], gasoline engine noise [22] and diesel engine noise [23].…”

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

“…Mohanaprasad 和 Arulmozhivarman [23,24] 在小波域采用 多种 ICA 代价函数实现了单通道回声消除. Park 等 [25] 用 ICA 方法消除了回声的非线性成分, Cheng 等 [26] 利 用 无 记 忆 非 线 性 的 级 数 展 开 和 独 立 向 量 分 析 (Independent Vector Analysis, IVA) 的方法, 将各阶基 函数视作参考信号来实现非线性回声抵消.…”