Enhancement of the ill-conditioned original recordings using novel ICA technique

The independent component analysis (ICA) method proposed in this study uses FastICA algorithm to improve the quality of the original recordings, which can be used as valuable pre-processing technique in signal processing methods. Initially, the ill-conditioned original audio recordings are separated using ICA methods and later, they are reconstructed using modified un-mixing matrix. The simulation results showed huge improvement of the original signal after reconstruction. The new method is found to be good because the accuracy is more compared to others in terms of the variance of the Gain matrix. The proposed method has potential applications in audio and biosignal processing techniques.

[1]  Danilo P. Mandic,et al.  Post-Nonlinear Blind Extraction in the Presence of Ill-Conditioned Mixing , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  E. Oja,et al.  Independent Component Analysis , 2013 .

[3]  Andrzej Cichocki,et al.  Locally Adaptive Algorithms for ICA and their Implementations , 2002 .

[4]  Kiyohiro Shikano,et al.  High-Fidelity Blind Separation of Acoustic Signals using SIMO-Model-based ICA with Information-Geometric Learning , 2003 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Walter Kellermann,et al.  A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments , 2006, Signal Process..

[7]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[8]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[9]  Wei Liu,et al.  Blind Extraction of Noisy Events using Nonlinear Predictor , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[10]  Walter Kellermann,et al.  Exploiting Narrowband Efficiency for Broadband Convolutive Blind Source Separation , 2007, EURASIP J. Adv. Signal Process..

[11]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[12]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[13]  Ju Liu,et al.  Speech Signal Enhancement Based on MAP Algorithm in the ICA Space , 2008, IEEE Transactions on Signal Processing.

[14]  Robert Aichner,et al.  POST-PROCESSING FOR BSS ALGORITHMS TO RECOVER SPATIAL CUES , 2006 .

[15]  Kiyohiro Shikano,et al.  Evaluation of SIMO Separation Methods for Blind Decomposition of Binaural Mixed Signals , 2005 .

[16]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[18]  Witold Kinsner,et al.  Separation performance of ICA on simulated EEG and ECG signals contaminated by noise , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[19]  Zbynek Koldovský,et al.  Optimal pairing of signal components separated by blind techniques , 2004, IEEE Signal Processing Letters.

[20]  Zhang Yi,et al.  Robust extraction of specific signals with temporal structure , 2006, Neurocomputing.

[21]  Noboru Murata,et al.  An Approach to Blind Source Separation of Speech Signals , 1998 .

[22]  Gene H. Golub,et al.  Matrix computations , 1983 .

[23]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.