Generalized multichannel frequency-domain adaptive filtering: efficient realization and application to hands-free speech communication

摘要

In unknown environments where we need to identify, model, or track unknown and time-varying channels, adaptive filtering has been proven to be an effective tool. In this contribution, we focus on multichannel algorithms in the frequency domain that are especially well suited for input signals which are not only auto-correlated but also highly cross-correlated among the channels. These properties are particularly important for applications like multichannel acoustic echo cancellation. Most frequency-domain algorithms, as they are well known from the single-channel case, are derived from existing time-domain algorithms and are based on different heuristic strategies, e.g, for stepsize normalization. Here, we present a new rigorous derivation of a whole class of multichannel adaptive filtering algorithms in the frequency domain based on a recursive least-squares criterion. Then, from the normal equation, we derive a generic adaptive algorithm in the frequency domain. Due to the rigorous approach, the proposed framework inherently takes the coherence between all input signal channels into account. An analysis of this multichannel algorithm shows that the mean-squared error convergence is independent of the input signal statistics (i.e., both auto-correlation and cross-correlation). A useful approximation provides interesting links between some well-known algorithms for the single-channel case and the general multichannel framework. We also give design rules for important parameters to optimize the performance in practice. The computational complexity is kept low by introducing several new techniques, such as a robust recursive Kalman gain computation in the frequency domain and efficient fast Fourier transform (FFT) computation tailored to overlapping data blocks. Simulation results and real-time performance for applications such as multichannel acoustic echo cancellation show the high efficiency of the approach.