Convolutive Blind Source Separation Methods

In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks

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

Permutation of the outputs at different frequency bins remains as a major problem in the convolutive blind source separation (BSS). In this work a coupled Hidden Markov model (CHMM) effectively exploits the psychoacoustic characteristics of signals to mitigate such permutation. A joint diagonalization algorithm for convolutive BSS, which incorporates a non-unitary penalty term within the crosspower spectrum-based cost function in the frequency domain, has been used. 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 has been substantially reduced, and demonstrated using a number of simulation studies

A Fast and Efficient Frequency-Domain Method for Convolutive Blind Source Separation

In this paper, the problem of blind separation of a convolutive mixture of audio signals is considered. A fast and efficient frequency-domain blind source separation (BSS) method using Independent component analysis (ICA) is investigated. The main difficulties of this approach lie in the so called permutation and amplitude problems. In order to solve the permutation ambiguity, the final value of the ICA derived separation matrix of one frequency bin, is used to initialize the ICA iterations in the next frequency bin. The amplitude problem is addressed by utilizing the elements in the inverse of the separation matrix. Experimental results demonstrate that successful separation is achieved and compared with conventional frequency-domain BSS methods, it is less computationally complex and has faster convergence