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In underwater acoustic communication, the transmitted signals are severely influenced by the reflections from both the sea surface and the sea bottom. As very large reflection signals from these boundaries cause an inter-symbol interference (ISI) effect, the communication quality worsens. A channel estimation-based equalizer is usually adopted to c...
The equalization of reverberation effects is essential for spa-tial soundfield reproduction, but estimation of the reverberant chan-nel presents several challenges to existing equalization techniques. This paper presents a method of active acoustic echo cancellation (AEC) for soundfield reproduction applications, using a modal de-scription of the r...
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... Section 4.5) which converges quickly and is computationally efficient. Chapter 5 discusses different possibilities for combinations of subsystems for AEC and LRC and the respective mutual influences of these subsystems [GKMK06a,GKMK07]. Main contributions in this chapter are the system identification and the influences on the LRC approaches 1 Introduction [GKMK08c,GKMK08d] and the identification of equalized impulse responses [GXJ + 11], as well as a method to increase LRC robustness based on the knowledge of the AEC convergence state [GKMK08b] (cf. ...
... The unmodelled tail of the RIR, which is depicted in Figure 3.1 in gray for a filter order of 1024 exemplarily, always leads to a residual echo at the output of the AEC. For correlated input signals this so-called tail-effect of acoustic echo cancellation, furthermore, leads to a biased system identification which gets more severe for multiloudspeaker hands-free systems [BMS98b,Kal07,GKMK07]. ...
... The nature of the tail will of course have influence on the typical estimation error and, thus, have influence on the LRC filter error. Secondly, even the RIR estimate of the first L AEC filter coefficients will be biased due to the influence of the unmodelled tail [BMS98b,Kal07,GKMK07] With the definition of the AEC system misalignment vector in (3.1.1) the RIR can be split up in two parts (cf. ...
... 4(d) that sparse IRs can be achieved by equalization and, thus, the application of proportionate filter update schemed may be advantageous.The proportionate normalized least-mean-squares (PNLMS) algorithm[10,26]differs from the NLMS algorithm by the fact that the available adaptation energy is distributed unevenly over all filter coefficients, i.e. each coefficient is updated with an adaptation gain proportional to its own magnitude. The underlying idea was originally presented in[27]based on the assumption that typical RIRs decay exponentially. Since in practice, the real magnitude of each coefficient is not known in advance for arbitrary IRs, the current LRC filter coefficients will be used in the PNLMS approach instead. ...
Hands-free telecommunication systems usually employ subsystems for acoustic echo cancellation (AEC), listening-room compensation (LRC) and noise reduction in combination. This contribution discusses a combined system of a two-stage AEC filter and an LRC filter to remove reverberation introduced by the listening room. An inner AEC is used to achieve initial echo reduction and to perform system identification needed for the LRC filter. An additional outer AEC is used to further reduce the acoustic echoes. The performance of proportionate filter update schemes such as the so-called proportionate normalized least mean squares algorithm (PNLMS) or the improved PNLMS (IPNLMS) for system identification of equalized impulse response (IR) are shown and the mutual influences of the subsystems are analyzed. If the LRC filter succeeds in shaping a sparse overall IR for the concatenated system of LRC filter and room impulse response (RIR), the PNLMS performs best since it is optimized for the identification of sparse IRs. However, the equalization may be imperfect due to channel estimation errors in periods of convergence and due to the so-called tail-effect of AEC, i.e. the fact that only the first part of an RIR is identified in practical systems. The IPNLMS is more appropriate in this case to identify the equalized IR.
... Here, L c,AEC is the length of the AEC filter which equals L ˜ h and is, in general, smaller than the length of the RIR L h . Thus, the so-called tail of the RIR which cannot be identified by the AEC always contributes to the estimation error˜herror˜ error˜h[k] and leads to a decreased performance of the equalizer [13]. ...
Dereverberation of speech signals in a hands-free scenario by inverse filtering has been a research topic for several years now. However, it is still a challenging problem because of the nature of common room impulse responses (RIRs), which are time-variant mixed phase systems having a large number of zeros close to, on, and even outside the unit circle in the z-domain. In this contribution an adaptive multi-channel equalization algorithm based on a decoupled version of the modified filtered-X LMS (mFxLMS) will be derived in the partitioned frequency domain. This new algorithm allows for fast convergence, computationally efficient implementation, and a low system delay under realistic conditions such as ambient noise and imperfect RIR estimates.
