Blind Source Separation and Independent Component Analysis: A Review

By Soo-Young Lee. 2005

272 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Eliya Nachmani
2. Lior Wolf
3. Yossi Adi
4. Yi Luo
5. Nima Mesgarani
6. Zhuo Chen
7. Alexandre D'efossez
8. Wotao Yin

Citations in the corpus

• Voice Separation with an Unknown Number of Multiple Speakers (2020, methodology): “, 2018), where traditionally signal processing techniques were applied to the task (Choi et al., 2005).”
• Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation (2018, neutral): “by directly formulating the separation in the time domain. Previous studies have explored the feasibility of time-domain speech separation through methods such as independent component analysis (ICA) [17] and time-domain non-negative matrix factorization (NMF) [18]. However, the performance of these”
• Speaker-Independent Speech Separation With Deep Attractor Network (2017, methodology): “different target tasks. In statistical methods, the target speech signal is modeled with probability distributions such as complex Gaussian [9] or methods such as independent component analysis (ICA) [10], where the interference signal is assumed to be statistically independent from the target speec”
• A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion (2013, methodology): “Two classical approaches for BSS are principal component analysis (PCA) [62] and independent component analysis (ICA) [18].”
• Robust artifactual independent component classification for BCI practitioners (2014, background): “Because a thorough analysis of the achievable separation performance is out of the scope of this paper, we refer the reader to [17, 23, 24] on the question of which ICA variants are”
• Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, unknown): “[39] for a review).”