Abstract
Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this paper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS) algorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has been in the original derivation. The presented algorithm formulation is applied to pruned transform-domain loudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have recently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond its generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block least-mean-squares algorithm.
Highlights
Acoustic echo cancellation (AEC) is generally necessary in full-duplex communication scenarios where loudspeaker echoes should be removed from a microphone signal
This paper presents a comprehensive derivation of the generalized frequency-domain adaptive filtering (GFDAF) algorithm as an approximation of the well-known block recursive least-squares (RLS) algorithm with exponential windowing
The following AEC scenario is considered: two loudspeaker signals (L = 2) carry a stereo recording of a speech signal superimposed by mutually uncorrelated white Gaussian noise signals such that a signal-to-noise ratio (SNR) of 20 dB results on average
Summary
Acoustic echo cancellation (AEC) is generally necessary in full-duplex communication scenarios where loudspeaker echoes should be removed from a microphone signal. Unlike the echo paths of telephone hybrids, acoustic echo paths are Schneider and Kellermann EURASIP Journal on Advances in Signal Processing (2016) 2016:6 described by significantly longer typically non-sparse impulse responses, as described by Hänsler in [24, 25] This increased complexity fueled the search for efficient frequency-subband [26, 27] and discrete Fourier transform (DFT)-domain algorithms [28,29,30], where multiple shorter adaptive filters or individual DFT bins, respectively, are adapted independently and lead to faster convergence and increased computational efficiency. Note that it is possible to reduce this delay on cost of computational efficiency by choosing an appropriate block-overlap for single-partition processing [4] On another track of research, a state-space model of the acoustic impulse responses was used to apply the concept of the Kalman filter to AEC [14, 33, 34].
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