We describe methods for applying Monte Carlo filtering and smoothing for estimation of unobserved states in a non-linear state space model. By exploiting the statistical structure of the model, we develop a Rao-Blackwellised Particle Smoother. Due to the lengthy nature of real signals, we suggest processing the data in blocks and a block-based smoother algorithm is developed for this purpose. All the algorithms suggested are tested with real speech and audio data and the results are shown and compared with those generated using the generic particle smoother and the extended Kalman filter. It is found that the suggested algorithms are more efficient in terms of memory useage and give better results.

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