Noise reduction in speech signals using adaptive independent component analysis (ICA) for hands free communication devices

This paper aims to remove the noise presents in speech signals during communication in all hands-free devices like mobile phone, video conferencing, teleconferencing conferencing etc. The existing noise reduction algorithms like an adaptive filter, time-varying and multiband adaptive gain control etc., have serious drawbacks. To enhance the algorithm for a better outcome an independent component analysis (ICA) based noise reduction is used. ICA is a statistical computational technique that divides the multisource signal into individual subcomponents. It is an active approach to cancel all of the ambient noise or a selective part of it without knowing the knowledge of the background noise. The adaptive nature of ICA in the proposed method makes the algorithm more robust in a real-time scenario. In the proposed method, the noisy speech signal is maximized by using kurtosis and negentropy cost functions of ICA to separate out the original speech signal from the noise. The simulations show that the proposed adaptive ICA method provides higher SNR compared to existing ICA methods and other conventional methods. Thus Adaptive ICA performs efficient noise cancellation in all real-time communication devices.