A coupled HMM for solving the permutation problem in frequency domain BSS

Saeid Sanei, Wenwu Wang, Jonathon A. Chambers

Abstract: Permutation of the outputs at different frequency bins remains as a major problem in convolutive blind source separation (BSS). A coupled hidden Markov model (CHMM) effectively exploits the psychoacoustic characteristics of signals to mitigate such permutations. A joint diagonalization algorithm has been used for convolutive BSS; it incorporates a non-unitary penalty term within the cross-power spectrum-based cost function in the frequency domain. The proposed CHMM system couples a number of conventional HMMs, equivalent to the number of outputs, by making state transitions in each model dependent, not only on its own previous state, but also on some aspects of the state of the other models. Using this method, the permutation effect is substantially reduced; it is demonstrated using a number of simulation studies.