Spelling suggestions: "subject:"nonlinear acoustic eco"" "subject:"onlinear acoustic eco""
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Nonlinear acoustic echo cancellationShi, Kun 10 November 2008 (has links)
The objective of this research is to presents new acoustic echo cancellation design methods that can effectively work in the nonlinear environment. Acoustic echo is an annoying issue for voice communication systems. Because of room acoustics and delay in the transmission path, echoes affect the sound quality and may hamper communications. Acoustic echo cancellers (AECs) are employed to remove the acoustic echo while keeping full-duplex communications. AEC designs face a variety of challenges, including long room impulse response, acoustic path nonlinearity, ambient noise, and double-talk situation. We investigate two parts of echo canceller design: echo cancellation algorithm design and control logic algorithm design. In the first part, our work focuses on the nonlinear adaptive and fast-convergence algorithms. We investigate three different structures: predistortion linearization, cascade structure, and nonlinear residual echo suppressor. Specifically, we are interested in the coherence function, since it provides a means for quantifying linear association between two stationary random processes. By using the coherence as a criterion to design the nonlinear echo canceller in the system, our method guarantees the algorithm stability and leads to a faster convergence rate. In the second part, our work focuses on the robustness of AECs in the presence of interference. With regard to the near-end speech, we investigate the double-talk detector (DTD) design in conjunction with nonlinear AECs. Specifically, we propose to design a DTD based on the mutual information (MI). We show that the advantage of the MI-based method, when compared with the existing methods, is that it is applicable to both the linear and nonlinear scenarios. With respect to the background noise, we propose a variable step-size and variable tap-length least mean square (LMS) algorithm. Based on the fact that the room impulse response usually exhibits an exponential decay power profile in acoustic echo cancellation applications, the proposed method finds optimal step size and tap length at each iteration. Thus, it achieves faster convergence rate and better steady-state performance. We show a number of experimental results to illustrate the performance of the proposed algorithms.
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Channel sparsity aware polynomial expansion filters for nonlinear acoustic echo cancellationVinith Vijayarajan (5930993) 16 January 2019 (has links)
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<p>Speech quality is a demand in voice commanded systems and in telephony. The
voice communication system in real time often suffers from audible echoes. In order to cancel
echoes, an acoustic echo cancellation system is designed and applied to increase speech quality
both subjectively and objectively.
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<p>In this research we develop various nonlinear adaptive filters wielding the new channel
sparsity-aware recursive least squares (RLS) algorithms using a sequential update. The
developed nonlinear adaptive filters using the sparse sequential RLS (S-SEQ-RLS) algorithm
apply a discard function to disregard the coefficients which are not significant or close to zero in
the weight vector for each channel in order to reduce the computational load and improve the
algorithm convergence rate. The channel sparsity-aware algorithm is first derived for nonlinear
system modeling or system identification, and then modified for application of echo
cancellation. Simulation results demonstrate that by selecting a proper threshold value in the
discard function, the proposed nonlinear adaptive filters using the RLS (S-SEQ-RLS) algorithm
can achieve the similar performance as the nonlinear filters using the sequential RLS (SEQ-RLS)
algorithm in which the channel weight vectors are sequentially updated. Furthermore, the
proposed channel sparsity-aware RLS algorithms require a lower computational load in
comparison with the non-sequential and non-sparsity algorithms. The computational load for the
sparse algorithms can further be reduced by using data-selective strategies.
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