An Improved Variable Structure Adaptive Filter Design and Analysis for Acoustic Echo Cancellation

In this research an advance variable structure adaptive Multiple Sub-Filters (MSF) based algorithm for single channel Acoustic Echo Cancellation (AEC) is proposed and analyzed. This work suggests a new and improved direction to find the optimum tap-length of adaptive filter employed for AEC. The structure adaptation, supported by a tap-length based weight update approach helps the designed echo canceller to maintain a trade-off between the Mean Square Error (MSE) and time taken to attain the steady state MSE. The work done in this paper focuses on replacing the fixed length sub-filters in existing MSF based AEC algorithms which brings refinements in terms of convergence, steady state error and tracking over the single long filter, different error and common error algorithms. A dynamic structure selective coefficient update approach to reduce the structural and computational cost of adaptive design is discussed in context with the proposed algorithm. Simulated results reveal a comparative performance analysis over proposed variable structure multiple sub-filters designs and existing fixed tap-length sub-filters based acoustic echo cancellers

Implementasi Echo Cancellation Menggunakan Algoritma Adaptif NLMS Pada DSP Card Seri TMS320VC33-150

ABSTRAKSI: ABSTRAK Fenomena Echo yang muncul di alam dapat juga terjadi pada jaringan telekomunikasi suara. Menyebabkan terjadi penurunan nilai QoS dari suara. Echo disebabkan ketidak serasian impedansi hybrid saat penyambungan 2 kabel jaringan lokal menuju 4 kabel jaringan central, disebut Circuit echo. Implementasi untuk menghilangkan echo digunakan metode Echo Cancellation. Filter adaptif dengan struktur filter FIR sebagai dasar membangun echo cancellation. Koefisien filter diadaptasi algoritma NLMS yang mampu menurunkan gradien noise dari algoritma LMS. Echo pada hybrid dimodelkan oleh delay dan redaman (3.5dB), dikonvolusikan terhadap sinyal input. Filter adaptif dibangun dengan konvolusi sinyal input terhadap bobot sebatas N (nilai taps filter) dan menghasilkan echo estimasi. Sinyal echo hybrid dikurangkan dengan echo estimasi, bila sama besar maka menghasilakn output yang bersih dari echo. Tetapi bila tidak sama besar maka menghasilkan residu (error), sinyal error menjadi komponen pembentuk bobot pada algoritma adaptif NLMS untuk mengadaptasi koefisien pada filter adaptif. Dengan tujuan meminimalkan rata-rata dari MSE (Mean Square Error). TMS320VC33 merupakan DSP (Digital Signal Processor’s) Card dengan kecepatan operasi 13,34 ns per instruksi, sehingga memungkinkan sistem berjalan real time dan meminimalkan pengaruh delay. Setelah pengujian implementasi echo cancelllation pada TMS320VC33, maka dihasilkan nilai step size (µ) optimum pada µ=0.6, saat MSE bernilai paling minimum. Kemudian waktu proses berdasarkan perhitungan duty cycle sebesar 39.87 ms untuk taps filter minimum. Nilai ERL sebesar 5.24 dB (standar CCITT Rec G.131, 6 ± 2.5 dB) dan nilai ERLE sebesar 17.65 dB (standar ITU-T G.168, 20 dB). Hasil dari sistem echo cancellation yang diimplementasikan masih sesuai dengan standar nilai-nilaiKata Kunci : Echo Cancellation, NLMS, Step SIZE, Taps Filter, TMS320VC33-150ABSTRACT: ABSTRACT Echo phenomenon usually appear in nature, also can be happen in voice telecomunication network. Which could decraesing voice QoS value. Echo couses by hybrid impedance mis’match, when built connections 2-wire at local loop to 4-wire at central, wich known as Circuit echo. Implementation use Echo Cancellation methode to make echo disappear. Adaptive filter use FIR filter structure as basic to built echo cancellation. Filter coefficient adapted by NLMS algorithm which able to decrease gradien noise from algorithm LMS. Echo in hybrid, models by delay and attenuation (3.5dB), then convoluted with input signal. Adaptive filter build by convolutioning input signal with weight which limited by N (taps filter value), to produce estimation echo. Hybrid echo signal subtract with echo estimation signal, if both of the signal have a same value, then it will produce output free from echo. But if the substraction of those signal produce the remainder then it’ll built error signal. The error signal use as a component to produce new weigth at the NLMS adaptive algorithm. The new weigths are use to update filter adaptive coefficient. Which aim to minimalize the average of MSE. While TMS320VC33 is DSP (Digital Signal Processor’s) Card which has operation valocity 13,34 ns to excute one instruction, so there’s probability for the system to operate as a real time system and minimalize the delay effect. After examine the implementation of echo cancelllation at TMS320VC33, then resulting step size (µ) value optimum at µ = 0.6, when MSE value very minimum. Then time process reach 39.87 ms base on duty cycle calculation, for minimum taps filter in use. ERL value result 5.24 dB where the CCITT Rec G.131 standard, 6 ± 2.5 dB. Then ERLE value result 17.65 dB where the ERLE ITU-T G.168 standard, 20 dB. The result of the implementation echo cancellation in TMS320VC33 still in range of the standard parameter values.Keyword: Echo Cancellation, NLMS, Step SIZE, Taps Filter, TMS320VC33-15

A study on adaptive filtering for noise and echo cancellation.

The objective of this thesis is to investigate the adaptive filtering technique on the application of noise and echo cancellation. As a relatively new area in Digital Signal Processing (DSP), adaptive filters have gained a lot of popularity in the past several decades due to the advantages that they can deal with time-varying digital system and they do not require a priori knowledge of the statistics of the information to be processed. Adaptive filters have been successfully applied in a great many areas such as communications, speech processing, image processing, and noise/echo cancellation. Since Bernard Widrow and his colleagues introduced adaptive filter in the 1960s, many researchers have been working on noise/echo cancellation by using adaptive filters with different algorithms. Among these algorithms, normalized least mean square (NLMS) provides an efficient and robust approach, in which the model parameters are obtained on the base of mean square error (MSE). The choice of a structure for the adaptive filters also plays an important role on the performance of the algorithm as a whole. For this purpose, two different filter structures: finite impulse response (FIR) filter and infinite impulse response (IIR) filter have been studied. The adaptive processes with two kinds of filter structures and the aforementioned algorithm have been implemented and simulated using Matlab.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .J53. Source: Masters Abstracts International, Volume: 44-01, page: 0472. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification

In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and the microphone can be strong and this generates echoes that can degrade user experience. Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable system and hence improve the perceived voice quality of a call. Traditionally, adaptive filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse responses (AIRs) using adaptive algorithms. The performances of a range of well-known algorithms are studied in the context of both AEC and network echo cancellation (NEC). It presents insights into their tracking performances under both time-invariant and time-varying system conditions. In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain and a frequency-domain AEC algorithms are proposed that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. As it will be shown later that the early part of the acoustic echo path is sparse while the late reverberant part of the acoustic path is dispersive, a novel approach to an adaptive filter structure that consists of two time-domain partition blocks is proposed such that different adaptive algorithms can be used for each part. By properly controlling the mixing parameter for the partitioned blocks separately, where the block lengths are controlled adaptively, the proposed partitioned block algorithm works well in both sparse and dispersive time-varying circumstances. A new insight into an analysis on the tracking performance of improved proportionate NLMS (IPNLMS) is presented by deriving the expression for the mean-square error. By employing the framework for both sparse and dispersive time-varying echo paths, this work validates the analytic results in practical simulations for AEC. The time-domain second-order statistic based blind SIMO identification algorithms, which exploit the cross relation method, are investigated and then a technique with proportionate step-size control for both sparse and dispersive system identification is also developed

New approaches for nonlinear acoustic echo cancellation

The nonlinearity of amplifier and/or loudspeaker gives rise to nonlinear echo in acoustic systems, which degrades seriously the performance of speech and audio communications. Many acoustic echo cancellation (AEC) schemes have been proposed by researchers to cancel the disturbing echo. In this thesis, two approaches for nonlinear echo cancellation, namely, the 2 nd order Volterra filter-based canceller and the sigmoid-transform-based (STB) canceller, are developed. Volterra filter (VF) plays a critical role in modeling a nonlinear acoustic system where the nonlinear distortion is mainly caused by a loudspeaker. However, the large number of coefficients and the high computational complexity always make the VF difficult to be used in practice. By analyzing a general 2 nd order VF model and a cascade model consisting of a 2 nd order VF and a transversal filter, this thesis proposes a simplified 2 nd order VF structure with relatively low computational complexity for the echo canceller, which is shown to be more efficient in acoustic echo cancellation applications. A theoretically justification is also provided to show the feasibility of such a simplification. Moreover, a normalized least mean square (NLMS) algorithm for kernel-separated 2 nd order VF is derived to accelerate the convergence speed of the coefficients of the nonlinear filter. This algorithm uses a new range of the step size or called convergence factor to ensure the stability of the adaptive filter. The outstanding performance of the proposed AEC is verified by computer simulations For solving the nonlinear distortion caused mainly by an amplifier, a simple yet efficient nonlinear echo cancellation scheme is proposed by using an adaptable sigmoid function in conjunction with a conventional transversal adaptive filter. The new scheme uses the least mean square (LMS) algorithm to update the sigmoid function and the recursive least square (RLS) algorithm to determine the weight vector of the transversal filter. The proposed acoustic echo canceller is proved to be convergent under some reasonable assumptions. Extensive computer simulations show that the proposed AEC has a very satisfactory echo cancellation performance for saturation-type nonlinear distortio

Simultaneous Transmission and Reception: Algorithm, Design and System Level Performance

Full Duplex or Simultaneous transmission and reception (STR) in the same frequency at the same time can potentially double the physical layer capacity. However, high power transmit signal will appear at receive chain as echoes with powers much higher than the desired received signal. Therefore, in order to achieve the potential gain, it is imperative to cancel these echoes. As these high power echoes can saturate low noise amplifier (LNA) and also digital domain echo cancellation requires unrealistically high resolution analog-to-digital converter (ADC), the echoes should be cancelled or suppressed sufficiently before LNA. In this paper we present a closed-loop echo cancellation technique which can be implemented purely in analogue domain. The advantages of our method are multiple-fold: it is robust to phase noise, does not require additional set of antennas, can be applied to wideband signals and the performance is irrelevant to radio frequency (RF) impairments in transmit chain. Next, we study a few protocols for STR systems in carrier sense multiple access (CSMA) network and investigate MAC level throughput with realistic assumptions in both single cell and multiple cells. We show that STR can reduce hidden node problem in CSMA network and produce gains of up to 279% in maximum throughput in such networks. Finally, we investigate the application of STR in cellular systems and study two new unique interferences introduced to the system due to STR, namely BS-BS interference and UE-UE interference. We show that these two new interferences will hugely degrade system performance if not treated appropriately. We propose novel methods to reduce both interferences and investigate the performances in system level.Comment: 20 pages. This manuscript will appear in the IEEE Transactions on Wireless Communication

Echo Cancellation : the generalized likelihood ratio test for double-talk vs. channel change

Echo cancellers are required in both electrical (impedance mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The optimum likelihood ratio test (LRT) for this problem was studied in a recent paper. The LRT requires a priori knowledge of the background noise and double-talk power levels. Instead, this paper derives a generalized log likelihood ratio test (GLRT) that does not require this knowledge. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change, based upon a single look. However, detection based on about 200 successive samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01) for the theoretical GLRT model. Application of a GLRT-based echo canceller (EC) to real voice data shows comparable performance to that of the LRT-based EC given in a recent paper

Echo Cancellation - A Likelihood Ratio Test for Double-talk Versus Channel Change

Echo cancellers are in wide use in both electrical (four wire to two wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian stochastic input signal model. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, post-detection integration of approximately one hundred sufficient statistic samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01)