DESIGN OF NORMALIZED SUBBAND ADAPTIVE FILTER FOR ACOUSTIC ECHO CANCELLATION

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1 DESIGN OF NORMALIZED SUBBAND ADAPTIVE FILTER FOR ACOUSTIC ECHO CANCELLATION Pavya.S, Graduate Student Member IEEE, K.S.Rangasamy college of technology, Tiruchengode,tamilnadu,India P.Babu,Associate professor(ece), K.S. Rangasamy College of Technology, Tiruchengode, Tamilnadu, India ABSTRACT: In hands-free telephones and teleconferencing systems, acoustic echo cancellers are required, which are often implemented by adaptive s. In these applications, the speech input signal of the adaptive is highly correlated and the impulse response of the echo path is very long. These characteristics will slow down the convergence rate of the adaptive if the well-known normalized leastmean- square (NLMS) algorithm is used. The normalized subband adaptive (NSAF) offers a good solution to this problem because of its decorrelating property. This paper proposes a VHDL implementation of normalized subband adaptive structure(nsaf) adaptive algorithm.the experiment result shows that the power consumed by subband is lower than that of full band. Index Terms Acoustic echo canceller, adaptive combination, normalized subband adaptive (NSAF), hands-free telephone, teleconferencing system Cascade intergrator-comb (). I.INTRODUCTION A common problem encountered in hands-free telephones and teleconferencing systems is the presence of echoes which are generated acoustically by the coupling between the loudspeaker and the microphone via the impulse response of a room [1]. Removal of these echoes requires the precise knowledge of the impulse response of the acoustic echo path, which may be time varying. In recent years, there has been a great interest in the use of adaptive s as acoustic echo cancellers to remove echoes [2], [3]. An adaptive can be characterized by its structure and adaptive ing algorithm [4]. The transversal with the well-known normalized least-mean-square (NLMS) algorithm is one of the most popular adaptive s because of its simplicity and robust performance [5], [6]. In acoustic echo cancellation (AEC) applications, however, the speech input signal of the adaptive is highly correlated and the impulse response of the acoustic echo path is very long. These two characteristics will slow down the convergence rate of the acoustic echo canceller if the NLMS-based adaptive is used to remove echoes. One technique to solve the above problem is subband adaptive ing. In conventional subband adaptive s (SAFs), each subband uses an individual adaptive sub in its own adaptation loop, which decreases the convergence rate of SAFs because of the aliasing and band-edge effects [7]. To solve these structural problems, a family of SAFs based on a new structure has been proposed [8] [10]. In [8], Courville and Duhamel derived an SAF using a weighted criterion. Based on the polyphase decomposition, Pradhan and Reddy [9] derived another SAF using a similar weighted criterion. In [10], Lee and Gan presented a normalized SAF (NSAF) from the principle of minimum disturbance, with its complexity close to that of the NLMS-based adaptive. The NSAF can be viewed as a subband generalization of the NLMSbased adaptive. Its central idea is to use the subband signals, normalized by their respective subband input variances, to update the tap weights of a fullband adaptive. This strategy leads to the decorrelating property of the NSAF [11]. Although these SAFs can obtain fast convergence rate when applied to AEC applications, they all require a tradeoff between fast convergence rate and small steady-state mean-square error (MSE) because of the use of a fixed stepsize. Several studies have shown that the setmembership, variable regularization parameter, and variable step-size parameter versions of the NSAF can offer possible schemes to overcome the conflicting requirements of fast convergence rate and small steadystate MSE [12] [14]. However, they all need to estimate the system noise power in advance, which increases the complexity of the system. Recently, an adaptive combination of fullband adaptive s has been proposed in [15], and its mean square error has been analyzed in [16]. The merit of this combination is that it can obtain both fast convergence rate and small steady-state MSE without estimate of the system noise power. More recently, a combination of subband adaptive s for AEC has been proposed [17], which is based on a conventional subband structure. In this paper, we propose a new 1232

2 subband adaptive combination scheme to deal with the tradeoff problem encountered in acoustic echo cancellers which are implemented by NSAFs. The proposed combination is carried out in subband domain and the mixing parameter that controls the combination is adapted by means of a stochastic gradient algorithm which employs the sum of squared subband errors as the cost function. The section is splited as II.NSAF structure III.Filter Bank IV. decimator V. interpolator VI.simulation results III.FILTER BANK A bank is a set of bandpass s with a common input for the analysis bank or a summed output for the synthesis bank.figure 2 shows an N-channel (or N-band) bank using z-domain notation, where H i (z) and G i (z) are the analysis and Noise Echo path + Analysis decimator + _ Σ Analysis Adaptive decimator Output bit error ratio Synthesis interpolator Fig 1: Block Diagram of NSAF II.NSAF STRUCTURE An N-channel analysis bank is a structure transforming an input signal to a set of N subband signals. A corresponding N-channel synthesis bank transforms N subband channels to a full band signal, in other words the inverse operation takes place. All subbands have equal bandwidth and the same decimation and interpolation factors. In this architecture both the microphone signal d(n) and the far-end signal u(n) are partitioned into N sub band signals via the analysis s H i (z ),i= 0,1, N-1 The sub band signals d i ( n) and y i ( n) for i = 0,1, N 1 are all critically decimated to a lower sampling rate. The decimated subband error signals are then defined as e id (n)=d i,d (n)-y i,d (n), i=0,1,..,n-1 Here we use the variable n and k to index the original and decimated sequences. Fig 2: NSAF Structure synthesis s, respectively, and the variable i = 0, 1,...,N 1 is used as the subband index. The analysis bank partitions the incoming signal X(z) into N subband signals X i (z), each occupying a portion of the original frequency band. The synthesis bank reconstructs the output signal Y(z) from N subband signals Y i (z) to approximate the input signal. A bank is called a uniform bank if the center frequencies of bandpass s are uniformly spaced and all s have equal bandwidth. By decomposing a fullband signal using an N-channel uniform bank, each subband signal X i (z) contains only 1/N of the original spectral band. Since the bandwidth of the subband signals X i (z) is 1/N of the original signal X(z), these subband signals can be decimated to 1/N of the original sampling rate while preserving the original information. A bank is called a critically (or maximally) decimated bank,if the decimation factor is equal to the number of subbands, i.e. D = N. Critical decimation preserves the effective sampling rate with N decimated subband signals, X i,d (z), each with 1/N of the original sampling 1233

3 rate, so that the total number of subband samples is identical to that of the fullband signal X(z). In the synthesis section, the decimated subband signals Xi,D(z) are interpolated by the same factor before being combined by the synthesis bank. Therefore, the original sampling rate is restored in the reconstructed fullband signal Y(z). IV. DECIMATOR Cascaded integrator-comb (), or Hogenauer s, are multirate s used for realizing large sample rate changes in digital systems. Both decimation and interpolation structures are supported by the Core. s are multiplierless structures, consisting of only adders, subtractors and registers. They are typically employed in applications that have a large excess sample rate. That is, the system sample rate is much larger than the bandwidth occupied by the signal. s are frequently used in digital downconverters (DDCs) and digital up-converters. Figure 1 shows the basic structure for a decimation. The integrator section consists of N ideal integrator stages operating at the high sampling rate fs. Each stage is implemented as a one-pole with a unity feedback coefficient The transfer functions for a single integrator is H(z)= H i (z) H c (z) =(1-z RM ) N /(1-z -1 ) N RM 1 =[ z -k ] N k=0 From the last form of the transfer function, we observe that the is equivalent to a cascade of N uniform FIR (finite impulse response) stages with unit coefficients; that is, the is equivalent to a cascade of N box-car s. The decimator is actually implemented using the pipelined architecture The pipeline registers P0, P1, P2 and P3 shorten the critical path through the differentiator cascade V. INTERPOLATOR Exchanging the integrator cascade with the differentiator cascade, as shown in Figure 3, produces a interpolator. Data is presented to the at the rate f s /R where it is processed by the differentiators. The rate expander in the figure causes a rate increase by a factor R by inserting R-1 zero valued samples between consecutive samples of the comb section output. The up-sampled and ed data stream is presented to the output at the sample rate fs. Just like the decimator, the Core implementation uses a pipelined structure. H i (z)=1/1-z -1 (1) o/p C C C R I I I o/p I I I R C C C Fig 3: Decimator Fig 4: Interpolator The comb section operates at the low sampling rate f s /R where R is the integer rate change factor. This section consists of comb stages with a differential delay of M samples per stage. The differential delay is a design parameter used to control the s frequency response. M is restricted to be either 1 or 2. The transfer function for a single comb stage, referenced to the high input sample rate is H c (z)=1-z RM (2) There is a rate change switch (indicated in the figure as the decimation function) between the two sections. The decimator subsamples the output of the last integrator stage, reducing the sample rate from fs to fs/r. The system transfer function for the composite, referenced to the high sampling rate, is VI.SIMULATION RESULT Analysis and Synthesis prototype s with length 16 taps is choosen. For design we use a method from [3]. The decimation factor L is 2. All calculation are based on a sampling rate of 8 khz. The echo-path is modeled by FIR-. The FIR impulse response has a reverberation time of 72 ms and was measured in a large car. As excitation signal we use colored noise, which is generated by first order IIR ing of white noise, and speech. Here five stage decimator and interpolator is used. It Supports decimation and interpolation rate changes between 8 and 16,383. Number of stages programmable between 1 and 8.Multiplierless architecture is ideal for systems. Output of the decimator is given in fig 6.output is nothing but the 1234

4 two s complement of the output.fig 7 discuss the output of the interpolator which is nothing but zero padding and shiffting right by 2.fig 8 gives the output of the synthesis which is nothing but the reconstructed output 80 percent of the input is obtained using the bit error ratio.power is calculated using synopsys tool and is shown in fig 9. Fig 8: vhdl output for synthesis Types of adaptive Power consumption(µw) algorithm Full band subband Tabel 1: comparison of power Fig 5: vhdl output analysis (length=16, direct form fir ) Fig 6: vhdl output for decimator(decimation factor=5) Fig 7:vhdl output for interpolator(interpolation factor=5) Fig 9:synopsys output VI CONCLUSION There are number of adaptive algorithms available in literature and every algorithm has its own properties, but aim of every algorithm is to achieve minimum mean square error at a higher rate of convergence with lesser complexity. In this paper, we focused on power consumption using synopsys EDA tool.power consumped by full band is µw and the power consumed by subband is µw. REFERENCES [1] M. M. Sondhi, The history of echo cancellation, IEEE Signal Process. Mag., vol. 23, no. 5, pp , Sep [2] J. Benesty, T. Gaensler, D. R. Morgan, M. M. Sondhi, and S. L. Gay, Advances in Network and 1235

5 Acoustic Echo Cancellation. Berlin, Germany: Springer-Verlag, [3] C. C. Kao, Design of echo cancellation and noise elimination for speech enhancement, IEEE Trans. Consumer Electronics, vol. 49, no. 4, pp , Nov [4] H. Ding, Fast affine projection adaptation algorithms with stable and robust symmetric linear system slovers, IEEE Trans. Signal Process., vol. 55, no. 5, pp , May [5] W. S. Gan, S. Mitra, and S. M. Kuo, Adaptive feedback active noise control headset: implementation, evaluation and its extensions, IEEE Trans. Consumer Electronics, vol. 51, no. 3, pp , Aug [6] A. H. Sayed, Adaptive Filters. New York: Wiley, [7] K. A. Lee, W. S. Gan, and S. M. Kuo, Subband Adaptive Filtering: Theory and Implementation. Hoboken, NJ: Wiley, [8] M. D. Courville and P. Duhamel, Adaptive ing in subbands using a weighted criterion, IEEE Trans. Signal Process., vol. 46, no. 9, pp , Sep [9] S. S. Pradhan and V. U. Reddy, A new approach to subband adaptive ing, IEEE Trans. Signal Process., vol. 47, no. 3, pp , Mar [10] K. A. Lee and W. S. Gan, Improving convergence of the NLMS algorithm using constrained subband updates, IEEE Signal Process. Lett., vol. 11, no. 9, pp , Sep [11] K. A. Lee and W. S. Gan, Inherent decorrelating and least perturbation properties of the normalized subband adaptive, IEEE Trans. Signal Process., vol. 54, no. 11, pp , Nov [12] M. S. E. Abadi and J. H. Husøy, Selective partial update and setmembership subband adaptive s, Signal Processing, vol. 88, no. 10, pp , Oct [13] J. Ni and F. Li, A variable regularization matrix normalized subband adaptive, IEEE Signal Process. Lett., vol. 16, no. 2, pp , Feb [14] J. Ni and F. Li, A variable step-size matrix normalized subband adaptive, IEEE Trans. Audio, Speech, Lang. Process., to be published, DOI: /TASL identification via adaptive combination of transversal s, Signal Process., vol. 86, pp , [16] J. Arenas-Garcia, A. R. Figueiras-Vidal, and A. H. Sayed, Mean-square performance of a convex combination of two adaptive s, IEEE Trans. Signal Process., vol. 54, no. 3, pp , Mar [17] L. A. Azpicueta-Ruiz, A. R. Figueiras-Vidal, and J. Arenas-García, Acoustic echo cancellation in frequency domain using combinations of s, in 19th Int. Congress on Acoustics (ICA), Madrid, Sep [18] N. J. Bershad, J. C. M. Bermudez and J.-Y. Tourneret, An affine combination of two LMS adaptive s Transient mean-square analysis, IEEE Trans. Signal Process., vol. 56, no. 5, pp , May [19] M. R. Petraglia, P. B. Batalheiro, Nonuniform subband adaptive ing with critical sampling, IEEE Trans. Signal Process., vol. 56, no. 2, pp , [20] J. Arenas-Garcia and A. R. Figueiras-Vidal, Adaptive combination of proportionate s for sparse echo cancellation, IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 6, pp , Aug [21] P. P. Vaidyanathan, Multirate Systems and Filter Banks. Englewood Cliffs, NJ: Prentice-Hall, BIOGRAPHIES Pavya.s, is final year ME-VLSI Design student in K.S.Rangasamy college of technology, Tiruchengode, Tamil nadu. Her area of interest is Digital signal processing and VLSI design techniques. Babu.P, M.E,(P.hD)., is working as Associate Professor in K.S.Rangasamy college of Technology, Tiruchengode, Tamil Nadu. His area of interest is Digital signal processing. [15] J. Arenas-García, M. Martínez-Ramón, A. Navia- Vázquez, and A. R. Figueiras-Vidal, Plant 1236