Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust M-estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference

For full-duplex hands-free acoustic human/machine interfaces, a combination of acoustic echo cancellation and speech enhancement is often required in order to suppress acoustic echoes, local interference and noise. In order to exploit positive synergies between acoustic echo cancellation and speech enhancement optimally, we previously presented a combined least-squares (LS) optimization criterion for the integration of acoustic echo cancellation and adaptive linearly-constrained minimum variance (LCMV) beamforming (Herbordt, W. et al., Proc. EURASIP European Sig. Process. Conf., 2004). By means of speech recognition experiments, we now illustrate the efficiency of the proposed solution in situations with high levels of background noise and with time-varying echo paths and frequent double-talk.

In this paper, we propose an acoustic model training technique which is robust against outliers such as clipping, unexpected noise, poorly pronounced word segments, or mistranscriptions, which deteriorate the quality of the acoustic models and in turn decrease speech recognition performance. The outlier-robust acoustic model training technique is based on a maximum likelihood (ML) criterion and automatically detects and removes outliers from the training data. Experiments with artificially contaminated mis-transcribed training data show that nearly the same word error rate can be obtained for contaminated data using the proposed technique as for uncontaminated data. Application to a dialogue speech database with unknown outliers reduces the errors by 4.03 %.

In this combustion, we embed full-duplex multichannel communication interfaces for tele-presence systems into a general framework. On the reproduction side, we consider a wide range of multichannel acoustic rendering techniques including traditional stereophony, '5.1' systems, and wave field synthesis using loud speaker arrays for sound immersion. On the recording side, microphone arrays are discussed for capturing clean desired signals with spatial information. Based on this general framework, real-time implementations of such full-duplex multichannel communication systems are then described. We combine wave field synthesis with multichannel acoustic echo cancellation and adaptive beamforming and discuss a real-time implementation on standard desktop and laptop PCs.

This contribution relates the robust generalized sidelobe canceller (RGSC) with an adaptive blocking matrix (ABM) after Hoshuyama et al. for non-stationary broadband signals with linearly constrained minimum variance (LCMV) beamformers. While alternative approaches only exploit spatial wave field characteristics, it is shown that the ABM introduces dependency on the desired signal power spectral density (PSD) into the array look-direction constraints. The ABM yields better interference suppression than for stationary frequency independent look-direction constraints, since the ABM does not suppress frequencies without desired signal components.

This paper analyzes positive synergies and theoretical limits of the combination of acoustic echo cancellation (AEC) and generalized sidelobe cancellers (GSC) for the removal of echoes of loudspeaker signals and local interferers. While the proposed system only requires one AEC for an arbitrary number of microphones, the array gain is limited by the number of sensor channels when all interferers arrive from different directions-of-arrival (DOAs). The paper also shows that the degrees of freedom of the adaptive sidelobe cancelling path are not sufficient when local interferers and acoustic echoes have common DOAs.

For hands-free acoustical human/machine interfaces, e. g. for automatic speech recognition or teleconferencing systems, microphone arrays using robust Generalized Sidelobe Cancellers (GSCs) in conjunction with acoustic echo cancellation (AEC) can be efficiently applied for optimum communication. This contribution devises a new structure for combining AEC and GSC. It reduces the computational complexity by more than a factor of ten relative to a time-domain arrangement, increases convergence speed, and preserves positive synergies. 1.

This paper deals with hands-free acoustical front-ends for human/machine interaction in competing talker situations using efficient frequency-domain implementations of a robust generalized sidelobe canceller (GSC). A new scheme is devised which leads to a factor of three of computational savings. The GSC tracking behavior is not impaired as the block lengths are kept sufficiently small.