We have investigated the possibility of separating signals from a single mixture of sources. This problem is termed the Monaural Separation Problem. Lars Kai Hansen has argued that this problem is topological tougher than problems with multiple recordings. Roweis has shown that inference from a Factorial Hidden Markov Model, with non-stationary assumptions on the source autocorrelations modelled through the Factorial Hidden Markov Model, leads to separation in the monaural case. By extending Hansens work we find that Roweis' assumptions are necessary for monaural speech separation. Furthermore we develop a Factorial hierarchical vector quantizer yielding a significant decrease in complexity of inference.