Subband techniques have been developed to use low order subfilters instead of full band higher order filters and consequently reduce the complexity and increase the convergence speed of the adaptive algorithm. In this paper the performance of two delayless subband adaptive algorithms for identification of an unknown system in an active noise control scheme are compared. This is carried out by using a common speech signal as the excitation input for identification of the secondary path model. The performances of the algorithms are measured in terms of the achieved minimum mean square error and misalignment error. The results are also compared to the time domain NLMS algorithm. The compared delayless structures are working in the closed loop form with DFT analysis filterbanks. Adaptation in the auxiliary loop and with help of weight transformation eliminates signal path delay and hence the unknown secondary path can be modelled accurately.