Blind Separation of Sources

By Ali Mansour. 1999

Subfields: Speech & Audio, Signal Processing

How researchers see this paper

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

In Speech & Audio

1. Aapo Hyvärinen
2. José M. Bioucas-Dias
3. Jose M. P. Nascimento
4. J. Urgen Schmidhuber
5. Timo Honkela
6. Erkki Oja
7. Michael Zibulevsky
8. Pau Bofill

In Signal Processing

1. Aapo Hyvärinen
2. José M. Bioucas-Dias
3. J. Urgen Schmidhuber
4. Jose M. P. Nascimento
5. Erkki Oja
6. Jaakko J. Väyrynen
7. Sepp Hochreiter
8. Timo Honkela

Citations in the corpus

• Information Theory, Inference, and Learning Algorithms (2004, unknown): “Further reading on blind separation, including non-ICA algorithms, can be found in (Jutten and Herault, 1991; Comon et al., 1991; Hendin et al., 1994; Amari et al., 1996; Hojen-Sorensen et al., 2002).”
• Underdetermined blind source separation using sparse representations (2001, background): “The problem of blind source separation [7], in the noiseless case, consists”
• Error awareness and the error-related negativity: evaluating the first decade of evidence (2012, methodology): “Independent Component Analysis (ICA, Jutten and Herault, 1991) has been successfully used to study single-trial properties of error-related brain potentials (Debener et al., 2005; Eichele et al., 2010; Wessel and Ullsperger, 2011).”
• WordICA—emergence of linguistic representations for words by independent component analysis (2010, methodology): “In the classic version of the ICA model (Jutten and Hérault 1991; Comon 1994; Hyvärinen et al. 2001), each observed random variable is represented as a weighted”
• A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion (2013, background): “Two classical approaches for BSS are principle component analysis (PCA) [62] and independent component analysis (ICA) [18].”
• Does independent component analysis play a role in unmixing hyperspectral data? (2003, background): “Independent component analysis (ICA) is an unsupervised source separation process [27] that has shown success in blind source separation, feature extraction, and unsupervised recognition [28].”
• GraRep: Learning Graph Representations with Global Structural Information (2015, background): “Examples include incremental SVD [22], independent component analysis (ICA) [7, 13], and deep neural networks [12].”
• Nonlinear ICA through low-complexity autoencoders (1999, background): “As a by-product, this encourages nonlinear independent component analysis (ICA) and sparse codes of the input data.”