This paper proposes a robust voice activity detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical characteristics of the instantaneous audio input. To further improve the performance of the system, an adaptive threshold is introduced, while a decision-smoothing scheme caters to the intra-frame correlation of speech signals. Extensive experiments under realistic scenarios support the proposed approach of combining several models for increased adaptation and performance.

A novel stable and robust algorithm for training of finite impulse response adaptive filters is proposed. This is achieved based on a convex combination of the least mean square (LMS) and a recently proposed generalised normalised gradient descent (GNGD) algorithm. In this way, the desirable fast convergence and stability of GNGD is combined with the robustness and small steady state misadjustment of LMS. Simulations on linear and nonlinear signals in the prediction setting support the analysis.

This letter proposes a novel stochastic gradient algorithm for the online adaptation of the pole position in Laguerre filters. The proposed algorithm exploits the inherent relationship between the values of the filter coefficients and the value of the Laguerre pole. This leads to an unbiased solution and, hence, a more accurate estimate of the error gradient. Simulations in a system identification setting support the analysis

The output error method, a fundamental technique for the updating of the coefficients of an adaptive IIR filter, is modified by introducing a time varying step size. The adaptation of this term is based on a gradient descent technique. This scheme can be considered as an extension of the algorithms presented in Benveniste et al. (1990) and Mathews et al. (1993) to IIR filters. The novel algorithm does not require any a priori knowledge of the statistical characteristics of the input signal and the unknown channel, since its step size converges automatically to its optimal value. This algorithm has the ability to converge in time-varying environments, which makes it suitable for processing of nonstationary signals.

A normalized robust mixed-norm (NRMN) algorithm for system identification in the presence of impulsive noise is introduced. The standard robust mixed-norm (RMN) algorithm, despite its ability to cope with impulsive noise by virtue of combining the first and second error norm in the cost function it minimizes, exhibits slow convergence, requires a stationary operating environment, and employs a constant step-size which needs to be determined a-priori. To overcome these limitations, the proposed NRMN algorithm introduces a time varying learning rate which is derived based upon the dynamics of the input signal, and thus no longer requires a stationary environment, a major drawback of the RMN algorithm. The normalized step-size is bounded from above and a parameter is introduced within its upper-bound, which provides a trade-off between the convergence rate and the steady-state coefficient error. The analysis and experimental results show that the proposed NRMN exhibits increased convergence rate and substantially reduces the steady-state coefficient error, as compared to the least absolute deviation (LAD) and RMN algorithms.