ICA-PSO-PE ile gerçek zamanlı gürültü giderimi

real-time implementable noise cancellation algorithm is developed. Speechand noise sources are not known but only their mixtures are observed. A mobileradio system is modelled with instantaneous mixture model as the environmentwhere noise cancellation is performed. A combination of independent componentanalysis (ICA) and particle swarm optimization (PSO) algorithms is used toseparate speech and noise signals. However, ICA has an ambiguity such that itis not possible to know which one of the separated signals is speech or noise. Toovercome this ambiguity problem, a pitch extraction (PE) algorithm is developedand combined with ICA-PSO. The ICA-PSO-PE algorithm is implemented inMATLAB. Signals are synthetically mixed with a mixing matrix and providedin frames of 40 ms to simulate the real-time behaviour. Pre-processing stepsexcept centering is bypassed to fasten the process and objective functions ofICA are slightly modied to reduce computational cost. Rule of convergence forPSO is changed in a way to rely on global best solution highly and a very smallswarm is used. In order to increase accuracy of separation, a learning period isintroduced. Experiments show that ICA-PSO-PE is a real-time implementableand robust noise cancellation algorithm in the sense that it is computationallyecient, accurately extracts speech signal from its mixtures, even with very lowSNR levels. The proposed noise cancellation algorithm is compared withFastICA by Hyv arinen et al and the subtraction method. Simulations show thatour algorithm outperforms FastICA in the sense of real-time implementabilityand outperforms subtraction method in the sense of robustness.