Constrained blind source separation : frequency domain independent component analysis with reference

• Main Author: Chen, Wenda.
• Other Authors: Rajapakse Jagath Chandana
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
• Online Access: http://hdl.handle.net/10356/15530
• Institution: Nanyang Technological University

Semi-blind separation of signals is useful for many applications in signal and image processing. Independent Component Analysis (ICA) method incorporates certain a priori knowledge of the interested sources and has been shown to be very useful in particular applications where the frequency of the estimated signal is known. While time-domain ICA needs to assume instantaneous mixtures and independent sources, frequency-domain ICA (FICA) is especially useful for solving convolutive mixtures of source signals since it can transform time domain convolution to a multiplication in the frequency domain. However, the permutation of the sources is exagerated in FICA because the individual demixing operations are used in different frequency bins and leads to different orders in the reconstruction process. In this report, we show how ICA with Reference (ICA-R) is extended to the frequency domain and convolutive mixtures as FICA-R, effectively solving the permutation problem in FICA while enhancing the optimization convergence using reference signals. We demonstrated the performance of our technique on synthetic data and real EEG and speech datasets. The results indicate that our proposed Frequency-domain ICA-R (FICA-R) is more effective and efficient than other FICAs and time-domain ICA-R in both convolutive and other types of mixtures.