A Fixed-point Ica Algorithm for Convoluted Speech Signal Separation

This paper describes a fixed-point independent component analysis (ICA) algorithm in combination with the null beamforming technique to sieve out speech signals from their convoluted mixture observed using a linear microphone array. The fixed-point algorithm shows fast convergence to the solution, however it is highly sensitive to the initial value from which iteration starts. A good initial value leads to faster convergence and yields better results. We propose the use of a null beamformer-based initial value for iteration and explore its effects on separation performance under different acoustic conditions by examining the noise reduction rate (NRR) and convergence speed. The result of the simulation confirms the efficacy and accuracy of the proposed algorithm.