When capturing speech signals using a distant microphone within a confined acoustic space, the recordings are often degraded by reverberation. This can have a detrimental impact on the quality and intelligibility of speech, especially when combined with acoustic noise. In recent years, there has been increasing demand for effective ways of combating the damaging effects of reverberation in applications such as hands-free telephony or hearing-aids technology. However, the task of providing a blind single-channel dereverberation method robust to high levels of noise and suitable for real-time processing remains a challenge. An important prerequisite for many single-channel dereverberation algorithms is the estimation of the acoustic parameters governing reverberation. In this thesis, a novel online method of estimating these parameters jointly with the interfering signal powers is proposed that is based on a combination of Voice Activity Detection and Extended Kalman Filters. This method is then extended to take into account the spectral structure of clean speech signals and to perform dereverberation by applying a time-frequency gain to the degraded speech spectrogram. The estimation of this gain is formulated as a Bayesian filtering problem conditioned on a Hidden Markov Model. In order to evaluate the proposed algorithm in terms of speech intelligibility, a novel algorithm for measuring Psychometric Functions efficiently in listening experiments is presented. The algorithms developed are evaluated on both simulated and real recordings and are compared with existing state-of-the art alternatives.