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

The Application of Blind Source Separation to Feature Decorrelation and Normalizations

We apply a Blind Source Separation BSS algorithm to the decorrelation of Mel-warped cepstra. The observed cepstra are modeled as a convolutive mixture of independent source cepstra. The algorithm aims to minimize a cross-spectral correlation at different lags to reconstruct the source cepstra. Results show that using "independent" cepstra as features leads to a reduction in the WER.Finally, we present three different enhancements to the BSS algorithm. We also present some results of these deviations of the original algorithm

Efficient Multiband Algorithms for Blind Source Separation

The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method

An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost function which is defined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures

A new second order method for blind signal separation from convolutive mixtures

This paper presents a new approach to separate colored signals mixed by FIR (finite impulse response) and MIMO (multiple-input multiple-output) channels. A cost function is proposed by employing linear constrainit to the de mixing vectors. The linear constraint is shown to be sufficient for avoiding trivial solution. The minimization of the cost function is performed using the Lagrangian method. Simulation results demonstrate the performance of the algorithm.<br /

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