The main aim of this thesis is to achieve low-complexity AEC for multichannel systems by developing efficient tap selection schemes for partially updating the AEC filters, and to develop model-based residual echo PSD estimators for improved RES. First, we propose novel tap selection schemes which exploit input signal sparsity across frequency, channels and time, leading to efficient partial updates of multichannel AEC filters. In particular, the proposed dynamic effort allocation scheme proportionately selects more filter taps in subbands and channels with larger magnitude tap-inputs while not ignoring the filters with smaller magnitude tap-inputs. Simulation results show that the proposed tap selection scheme achieves similar echo cancellation performance compared to full filter update at a significantly reduced computational cost (about 28%). Second, we propose novel signal-based methods to estimate the late residual echo PSD in online mode. The late residual echo PSD is modeled using an IIR filter on the PSD of the loudspeaker signal, based on frequency-dependent reverberation scaling and decay parameters. Third, we propose a novel model for the early residual echo PSD and combine it with the IIR filter model for the late residual echo PSD to yield a novel model for the residual echo PSD. In particular, we model the early residual echo PSD using a moving average filter on the PSD of the loudspeaker signal, based on a frequency-dependent coupling factor. We propose signal-based methods based on output error to jointly estimate all three model parameters by minimizing a single cost function in online mode. Simulation results show that the proposed output error method with the recursive prediction error algorithm outperforms state-of-the-art offline and online methods, yielding the best segmental speech-to-speech distortion ratio score (about 2-5 dB better), while also yielding the best segmental residual echo attenuation score (about 1-2 dB better).weiterlesen