Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement

Abstract

In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

New Variable Selected Coefficients Adaptive Sparse Algorithm for Acoustic System Identification

“…This algorithm is a combination between non-proportionate and proportionate NLMS adaptation. The update formula of the IP-NLMS is described by: 𝒘 𝒂 (𝑛 + 1)=𝒘 𝒂 (𝑛) + µ𝑄(𝑛)𝒔 𝒃 (𝑛) 𝑒(𝑛) 𝒔 𝒃 𝑻 (𝑛)𝑄(𝑛)𝒔 𝒃 (𝑛) + 𝜉 𝐼𝑃 (11) The diagonal control matrix 𝑄(𝑛) is used to determine exact step-size value of each coefficient. This matrix is defined as follows:…”

“…We cite for example, the basic Least Mean Square (LMS) and normalized LMS version which are extensively used especially for their simplicity of implementations. Other filtering algorithms have been proposed for AEC applications [9][10][11]. The problem of echo becomes more complex in situations where the excitation signal is strongly non-stationary and the echo path is variable.…”