A Robust Adaptive Beamformer with a Blocking Matrix Using Coefficient-Constrained Adaptive Filters

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1 640 IEICE TRANS. FUNDAMENTALS, VOL.E82 A, NO.4 APRIL 1999 PAPER A Robust Adaptive Beamformer with a Blocking Matrix Using Coefficient-Constrained Adaptive Filters Osamu HOSHUYAMA, Akihiko SUGIYAMA, and Akihiro HIRANO, Members SUMMARY This paper proposes a new robust adaptive beamformer applicable to microphone arrays. The proposed beamformer is a generalized sidelobe canceller (GSC)with a variable blocking matrix using coefficient-constrained adaptive filters (CCAFs). The CCAFs, whose common input signal is the output of a fixed beamformer, minimize leakage of the target signal into the interference path of the GSC. Each coefficient of the CCAFs is constrained to avoid mistracking. In the multipleinput canceller, leaky adaptive filters are used to decrease undesirable target-signal cancellation. The proposed beamformer can allow large look-direction error with almost no degradation in interference-reduction performance and can be implemented with a small number of microphones. The maximum allowable look-direction error can be specified by the user. Simulation results show that the proposed beamformer, when designed to allow about 20 of look-direction error, can suppress interference by more than 17 db. key words: beamforming, microphone array, adaptive signal processing, noise reduction 1. Introduction Microphone arrays have been studied for hands-free telephone, teleconferencing, hearing aid, speech recognition, and speech enhancement [1] [19]. Adaptive microphone arrays are especially promising technique [1], [8] [19]. They are based on adaptive beamforming such as generalized sidelobe canceller (GSC) and can attain high interference-reduction performance with a small number of microphones arranged in a small space [1]. Adaptive beamformers extract the signal from the direction of arrival (DOA) specified by the steering vector, a parameter of beamforming. However, with classical adaptive beamformers based on GSC, like simple Griffiths-Jim beamformer (GJBF) [8], target-signal cancellation occurs in the presence of steering-vector error. The error in the steering vector is inevitable because actual microphone arrays have imperfections. The array imperfections include errors in the microphone position, the microphone gain, and the target DOA (direction of arrival). For teleconferencing and hands-free telephone conversation in cars, the error in the target DOA is a major factor. Several signal processing techniques have been proposed to avoid target signal cancellation. These techniques are called robust beamforming since they are ro- Manuscript received April 3, Manuscript revised August 26, The authors are with C&C Media Research Laboratories, NEC Corporation, Kawasaki-shi, Japan. bust against errors. Unfortunately, they still have other problems such as degradation in interference-reduction performance, increase in the number of microphones, or mistracking. In this paper, a new robust adaptive beamformer to avoid these difficulties is proposed. The proposed beamformer uses a variable blocking matrix with coefficient-constrained adaptive filters (CCAFs). The following section describes the conventional robust beamformers based on GSC. The structure of the proposed beamformer is derived in Sect. 3. Simulations in Sect. 4 demonstrate the interference-reduction performance of the proposed beamformer with Gaussian signals and a real speech signal. 2. Robust Beamformers Based on Generalized Sidelobe Canceller 2.1 Generalized Sidelobe Canceller A structure of the GSC with M microphones is shown in Fig. 1. For simplicity, an equi-spaced broadside linear array with omnidimensional microphones is assumed. All the signals including the target and interference propagate as plane waves. Let us assume that the approximate target DOA is known, which is perpendicular to the array of microphones. The approximate target DOA is called look direction and the region of DOA which should be allowed as target DOA is called allowable DOA region. The GSC includes a fixed beam- Fig. 1 Generalized sidelobe canceller.

2 HOSHUYAMA et al: A ROBUST ADAPTIVE BEAMFORMER USING COEFFICIENT-CONSTRAINED ADAPTIVE FILTERS 641 former (FBF), a multiple-input canceller (MC), and a blocking matrix (BM). The FBF enhances the target signal. d(k) is the output signal of the FBF at sample index k, and x m (k) is the output signal of the m- th microphone (m = 0,...,M). The MC adaptively subtracts the components correlated to the output signals y m (k) of the BM, from the delayed output signal d(k Q) of the FBF, where Q is the number of delay samples for causality. The BM is a kind of spatial rejection filter. It rejects the target signal and passes interference. If the input signals y m (k) of MC, which are the output signals of the BM, contain only interference, the MC rejects the interference and extracts the target signal. However, if the target signal leaks into y m (k), target-signal cancellation occurs at the MC. The BM in the simple GJBF is sensitive to the steering-vector error and easily leaks the target signal. The vector error is caused by microphone arrangement error, microphone sensitivity error, look-direction error, and so on. In actual usage, the major factor of the steering-vector error is the look-direction error. This is because the target often changes its position because of the speaker s movement. It is impossible to know the exact DOA of the target signal. Thus, signal cancellation is an important problem. 2.2 Conventional Robust Beamformers Several approaches to avoid target-signal cancellation have been proposed [9] [19]. Some robust beamformers introduce constraints to the adaptive algorithm in the MCs. Adaptive algorithms with leakage [9], noise injection [10], or norm constraint [11] restrain the undesirable signal-cancellation. The beamformers pass the target signal in the presence of small steering-vector error. However, when they are designed to allow large look-direction error, which is often required for microphone arrays, a strict restraint is required. Such a strict restraint causes degradation in interference-reduction performance. Some robust beamformers use improved spatial filters in the BM [9], [12] [15]. The filters eliminate the target signal in the presence of steering-vector error. However, they have been developed to allow small lookdirection error. When they are designed to allow large look-direction error, the filters lose a lot of degrees of freedom for interference reduction. The loss in the degrees of freedom degrades interference-reduction performance. To avoid the performance degradation caused by this loss, additional microphones are needed. Target tracking or calibration is another approach for robust beamforming. It can allow large error without loss in the degrees of freedom and keep high interference-reduction performance. However, mistracking occurs with a burst signal such as speech. Moreover, precise target-tracking methods have to use matrix products, which require a lot of computations [16] [18]. A rough target-tracking method which does not need matrix products is proposed [19]. However, its tracking is not sufficiently precise when its parameters are set to allow a large tracking range. It may result in considerable target-signal cancellation or degraded interference-reduction performance. 3. Proposed Robust Beamformer The structure of the proposed robust beamformer is shown in Fig. 2. The proposed beamformer uses a new BM with CCAFs and an MC with leaky adaptive filters (LAFs). The CCAFs in the BM minimize the output signals of the BM. Because of the constraints in the CCAFs, this minimization leads to minimization of the target-signal leakage at the BM output. The minimization varies the spatial filtering pattern of the BM according to the target direction, resulting in target tracking. However, in the proposed beamformer, the tracking region is limited by the constraints of the CCAFs to avoid mistracking. Moreover, in the MC, LAFs are used as well as in [9]. The LAFs prevent the target-signal cancellation when the target-signal minimization at the BM is incomplete. In the BM, the CCAFs behave like adaptive noise cancellers. The input signal of each CCAF is the output signal of the FBF and the output of the CCAF is subtracted from the delayed microphone signal. The signal relationship in the BM with N-tap CCAFs is described by Fig. 2 The proposed beamformer.

3 642 IEICE TRANS. FUNDAMENTALS, VOL.E82 A, NO.4 APRIL 1999 y m (k) =x m (k P ) Hm T (k)d(k), (1) H m (k) =[h m,0 (k),h m,1 (k),...,h m,n 1 (k)] T, (2) D(k) =[d(k),d(k 1),...,d(k N + 1)] T, (3) (m =0, 1,...,M 1), where T denotes vector transpose. y m (k) is the m- th output signal of the BM and x m (k) is the m-th microphone signal. P is the number of delay samples for causality. H m (k) is the coefficient vector of the m-th CCAF and D(k) is the signal vector consisting of delayed signals of d(k), which is the output signal of the FBF. The CCAF coefficients h m (k) are adapted with coefficient constraints. The adaptation using the normalized-least-mean-squares (NLMS) algorithm is described as follows: h m,n(k+1) = h m,n (k)+β y m(k) d(k n), (4) D(k) 2 φ m,n for h m,n(k+1) >φ m,n h m,n (k+1) = ψ m,n for h m,n(k+1) <ψ m,n h m,n(k+1) otherwise (5) (m =0, 1,...,M 1)(n =0, 1,...,N 1), where denotes the Euclid norm. h m,n(k +1) are temporal coefficients for limiting functions, and β is the step size. φ m,n and ψ m,n are the upper and lower limits for each coefficient. In the output signal y m (k), the components correlated to d(k) are cancelled by the CCAFs. Each coefficient of the CCAFs is constrained based on the fact that filter coefficients for target-signal minimization vary significantly with the target DOA. An example of filter-coefficient variation is illustrated in Fig. 3. By the design of the constrained regions of the CCAFs, the maximum allowable look-direction error is specified. For example, when the CCAF coefficients are constrained in the hatched region in Fig. 3, up to 20 error in look direction could be allowed. Only the sig- Fig. 3 An example of CCAF coefficients to minimize signals from different DOAs and an example of CCAF constraints. nal that arrives from a DOA in the limited DOA region is minimized at the outputs of the BM and remains at the output of the MC. If no interference exists in the region, which is common with microphone arrays, no mistracking occurs. The adaptation of the CCAFs should be carried out while the signal-to-interference-plus-noise ratio (SINR) is high enough. This is because the target signal is the desired signal for the adaptation algorithm of the CCAFs. When the SINR is low, adaptation of the CCAFs could cause large misadjustment. Therefore, the adaptation of the CCAFs should be controlled according to the SINR. This adaptation mode control is similar to that of echo cancellers with a double-talk detector. In the MC, LAFs subtract the components correlated to y m (k), (m =0,...,M), from d(k Q). Let L be the number of taps in each LAF, and W m (k) and Y m (k) be coefficient vector and signal vector of the m-th LAF respectively. The signal processing in the MC is described by M 1 z(k) =d(k Q) Wm T (k)y m(k), (6) m=0 W m (k) =[w m,0 (k),w m,1 (k),...,w m,l 1 (k)] T, (7) Y m (k) =[y m (k),y m (k 1),...,y m (k L+1)] T, (8) (m =0, 1,...,M 1). Coefficients of the LAFs are updated by an adaptive algorithm with leakage. The adaptation with the NLMS algorithm is described as follows: W m (k+1) = W m (k) γw m (k) z(k) + α Y M 1 m (k) (9) Y m (k) 2 m=0 (m =0, 1,...,M 1) where α is the step size, and γ is the leakage factor which is a small positive constant. The leakage (γw m (k) in Eq. (9)) restrains excess growth of tap coefficients. The restraint inhibits undesirable target cancellation when the target-signal slightly leaks into the LAF inputs. The adaptations of the LAFs in the MC are performed during almost opposite periods to that of the CCAFs in the BM. This is because the relationships between the desired signal and the noise for the adaptation algorithm are contrary in the MC and the BM. For the LAFs, the SINR should be high in terms of the convergence speed and the final misadjustment. Because the proposed method loses no degree of freedom for interference reduction in the BM, it allows large look-direction error with a small number of microphones, thus keeps high interference-reduction perfor-

4 HOSHUYAMA et al: A ROBUST ADAPTIVE BEAMFORMER USING COEFFICIENT-CONSTRAINED ADAPTIVE FILTERS 643 (m =0, 1,...,M 1) where max{,, } means the maximum value among the three arguments. The variable T m is the group delay in samples obtained from the arrangement of the simulated array and maximum allowable look-direction errors. Each T m was roughly estimated by Fig. 4 Comparison in selectivity between LAF and CCAF. (Not quantitative) mance. The proposed beamformer requires small computations for its robustness because all the adaptations in the BM can be implemented without matrix product. The proposed method can be interepreted as a structure which replaces LAFs in the BM with the CCAFs [19]. Figure 4 illustrates qualitative comparison between the LAF and the CCAF with respect to look-direction error and coefficient error from the optimum for signal blocking. Both the CCAF and the LAF give error characteristics approximating the ideal nonlinearlity for target tracking. However, the nonlinearity of the CCAF is a better approximation to the ideal nonlinearity than that of the LAF as in Fig. 4. The coefficient error of the CCAF becomes effective only when the look-direction error exceeds the threshold, otherwise it has no effect. On the other hand, the coefficient error of the LAF varies continuously with the look-direction error. Therefore, the CCAF leads to precise target tracking, which results in less target-signal cancellation and sharper spatial selectivity. 4. Simulations To demonstrate the performance of the proposed method, simulations have been performed. The simulated array is 5-channel, linear, equally spaced, and with a microphone spacing of 4.0 cm. The sampling rate is 8kHz. The FBF used is a simple beamformer as d(k) = M 1 m=0 x m (k). (10) 4.1 Selection of Parameter Values The number of delay in samples P and Q can be determined as the group delay expected from the arrangement of the array. The constraints φ m,n and ψ m,n of the CCAFs were settled upon as follows: φ m,n = ψ m,n, 1 = π max{0.1, (n P ) T m, (n P ) T m }, (11) T m = b m f s sin( θ), (12) c where b m is the distance between the corresponding microphone and the center of the array, f s is the sampling rate (8kHz), c is the sound velocity (340 m/s), and θ is the maximum allowable look-direction error. Equation (11) was derived so that the constrained regions defined by φ m,n and ψ m,n cover the ideal interpolation function, which takes a form of sin{ }/{ }. (See Appendix). Selection of the step sizes, α and β, influences the sound quality. A small step size leads to slow convergence and slow tracking. Slow tracking with both the CCAFs and the LAFs results in breathing noise at the output. On the other hand, a large step size increases the final misadjustment. Misadjustment with the CCAFs causes target-signal cancellation and that with the LAFs leads to degradation in the interferencereduction performance. The step sizes used in the experiments were selected so that the subjective quality of the output signal was maximized. The leakage factor γ should be determined after the selection of the step sizes, because it dominates the interference-reduction performance and the targetsignal cancellation. The coefficient γ was determined by trial and error so that the target-signal cancellation was tolerable for speech communications. Further studies on the parameter selection is needed for different applications. 4.2 Results In the first simulation, sensitivities after convergence against single-signal DOA have been investigated. Band-limited ( khz) Gaussian signals were used. Assumed look direction was 0. The maximum allowable look-direction error used was 20. The adaptations of the CCAFs in the BM and LAFs in the MC were controlled as follows: First, the CCAFs were adapted for 50,000 iterations, and then the LAFs were adapted for 350,000 iterations. The number of coefficients of all the CCAFs and all the LAFs was 16. The parameters used were P =5,Q=10,α=0.2,β =0.1, and γ = The total output powers after convergence normalized by the power in the assumed look direction are plotted in Fig. 5. They indicate sensitivity for the signal as a function of its DOA. In the course of the convergence, the convergence speed was relatively fast in the beginning and became slower. In a few seconds after the start of the iterations, the total output power

5 644 IEICE TRANS. FUNDAMENTALS, VOL.E82 A, NO.4 APRIL 1999 Fig. 5 Normalized output power after convergence as a function of DOA. Fig. 7 Normalized output power after convergence as a function of DOA with different SINRs. Fig. 6 Sensitivity as a function of signal direction at different frequencies for an almost white signal. was reduced by about 10 db, and then it decayed slowly toward the final value. The plots in Fig. 5 are of the FBF (A), simple GJBF [8] (B), norm constrained method [11] (C), rough target-tracking method [19] (D), and the proposed method (E). The solid line E shows that the proposed beamformer achieves both the robustness against 20 look-direction error and high interference-reduction performance, which is 26 db at θ =±30. Sensitivities for some frequencies are also shown in Fig. 6. They illustrate that the frequency dependency of sensitivity is small and thus, the proposed method is suitable for broadband application such as microphone arrays. It should be noted that the proposed method achieves a small frequency dependency and high interference-reduction performance, even though the response of the employed FBF is frequency dependent and has high sidelobes. In the second simulation, sensitivities for different SINRs were investigated. The simulation was performed with a single signal source that stopped af- ter 50,000 iterations and a single continuous interference signal. This is a simplified simulation of burst characteristics like speech. Another bandlimited white Gaussian signal, which imitates an interference like airconditioner noise, existed throughout the simulation. The power ratio of the two is defined as the SINR. The adaptations of the CCAFs and LAFs were controlled in the same way as the first simulation. Figure 7 shows normalized output power after convergence. Lines C and D indicate that when SINR is higher than about 10 db, the target signal minimization is enough for the robustness. The third simulation was for a colored target signal. The colored signal was generated by processing a band-limited white Gaussian signal with a low pass filter having a transfer function: F (z 1 )=1/(1 0.9z 1 ). (13) Normalized output powers as a function of DOA θ are plotted in Fig. 8. The DOA region with low sensitivity is wider than that in Fig. 5. This indicates a problem; the allowable look-direction varies with the spectra of the target signal and interference. However, this problem is more serious for the norm-constrained method [11] than for the proposed method. For example, in Fig. 8, the width of the DOA region where the power is over 6dB is θ = ±52 for the normconstrained method. This width is 32 wider than that in Fig. 5. On the other hand, the width for the proposed method in Fig. 8is ±28, which is only 8 wider than in Fig. 5. The performance of the proposed beamformer is more stable than that of the norm-constrained method, although more stability may be needed for some applications. The total output powers for various coefficient constraints with the CCAFs are shown in Fig. 9. The signal was a bandlimited white Gaussian noise. The allowable target-direction errors are approximately 6, 10, and 20. These lines demonstrate that the allowable

6 HOSHUYAMA et al: A ROBUST ADAPTIVE BEAMFORMER USING COEFFICIENT-CONSTRAINED ADAPTIVE FILTERS 645 Fig. 8 Normalized output power after convergence as a function of DOA for a colored signal. Fig. 10 Effect of gain error and position error in microphone arrangement. Fig. 9 Normalized output power after convergence for different allowable target directions. target-direction error can be specified by the user. Robustness against other array imperfections than DOA error was also investigated. The signal was a bandlimited white Gaussian noise. Figure 10 shows normalized output powers with some array imperfections: ±3 db gain error, or ±3.0 mm position error. These errors are larger than those with actual arrays. Differences in the responses of the norm-constrained method and the proposed method from those without error are small and acceptable. They indicate that the robust beamformers including the proposed method are robust against array imperfections. However, the proposed method outperforms the norm-constrained method with the sharper response. Finally, a speech signal was used as the target signal, and a band-limited Gaussian signal as the interference. The target arrived from the direction having 10 error from the assumed look direction, and the interference arrived from the direction of θ =45. Adaptationmode control was performed by a simple method according to the output power of the FBF. While the output power was larger than a threshold, only the BM adaptation was carried out and while the power was smaller, only the MC was adapted. The threshold was fixed through the simulations. Figure 11 shows the speech signal used for the simulations and the output signals of the beamformers, which include FBF (b), the norm-constrained method (c), and the proposed method (d). Magnitude of a waveform during nospeech periods indicates the amount of residual interference. The proposed method shows better interference reduction than the norm-constrained method. The output powers for the speech and interference is ilustrated in Fig. 12. The output powers during no-speech periods indicates interference-reduction performance. Noise reduction by the norm-constrained method is 6 db and that by the proposed method is as much as 17 db. 5. Conclusion A new robust adaptive beamformer applicable to microphone arrays has been proposed. The proposed beamformer is a generalized sidelobe canceller using a variable blocking matrix with coefficient-constrained adaptive filters. The proposed beamformer can allow large look-direction error with almost no degradation in interference-reduction performance and can be implemented with a small number of additional computations. The maximum allowable look-direction error can be specified by the user. Simulations with a real speech shows that the proposed beamformer can suppress interference by more than 17 db when designed to allow about 20 of look-direction error. Acknowledgement The authors would like to thank Dr. Takao Nishitani, Deputy General Manager of Silicon System Research Laboratories (formerly Deputy General Manager of Information Technology Research Laboratories), NEC Corporation for his guidance and continuous encour-

7 646 IEICE TRANS. FUNDAMENTALS, VOL.E82 A, NO.4 APRIL 1999 Fig. 11 Speech signal for simulations and output signals for the speech and interference signal. (a)original target signal, (b) Output by Fixed Beamformer, (c)output by Norm-Constrained Method, (d)output by Proposed Method. Fig. 12 Output powers for the speech and interference signal. agement during the course of this research. References [1] J.L. Flanagan, D.A. Berkley, G.W. Elko, and W.M.M. Sondhi, Autodirective Microphone Systems, Acustica, pp.58 71, Feb [2] J.L. Flanagan, J.D. Johnson, R. Zahn, and G.W. Elko, Computer-steered microphone arrays for sound transduction in large rooms, J.A.S.A., vol.78, no.5, pp , Nov [3] M.M. Goodwin and G.W. Elko, Constant beamwidth beamforming, Proc. IEEE ICASSP 93, pp.i , [4] K. Nishikawa, H. Ohno, and T. Kanamori, Design of fan filter for wideband narrow beam forming, IEICE Technical Report, DSP93-92, Jan [5] Y. Kaneda and J. Ohga, Adaptive microphone array system for noise reduction, IEEE Trans. Acoust., Speech & Signal Process., vol.34, pp , June [6] F. Asano, Y. Suzuki, and T. Sone, Weighted RLS adaptive beamformer with initial directivity, IEEE Trans. SAP, vol.3, no.5, pp , Sept [7] Y. Nagata and H. Tsuboi, A study of noise reduction using two-channel adaptive beamformer, Proc. ASJ Spring Conf., 2-P-19, vol.i, pp , March [8] L.J. Griffiths and C.W. Jim, An alternative approach to linear constrained adaptive beamforming, IEEE Trans. Antennas & Propag., pp.27 34, Jan [9] I. Claesson and S. Nordholm, A spatial filtering approach to robust adaptive beamforming, IEEE Trans. Antennas & Propag., pp , Sept [10] N.K. Jablon, Adaptive beamforming with the generalized sidelobe canceller in the presence of array imperfections, IEEE Trans. Antennas & Propag., pp , Aug [11] H. Cox, R.M. Zeskind, and M.M. Owen, Robust adaptive beamforming, IEEE Trans. Acoust., Speech & Signal Process., pp , Oct [12] B. Widrow and M. McCool, A comparison of adaptive algorithms based on the methods of steepest descent and random search, IEEE Trans. Antennas & Propag., pp , Sept [13] M.H. Er and A. Cantoni, Derivative constraints for broadband element space antenna array processors, IEEE Trans. Acoust., Speech & Signal Process., pp , Dec [14] M.H. Er and A. Cantoni, An unconstrained partitioned realization for derivative constrained broad-band antenna array processors, IEEE Trans. Acoust., Speech & Signal Process., pp , Dec [15] G.L. Fudge and D.A. Linebarger, Steered response control of the generalized sidelobe canceller, Proc. IEEE ICASSP 95, pp , May [16] S. Affes, S. Gazor, and Y. Grenier, Robust adaptive beamforming via LMS-like target tracking, Proc. IEEE ICASSP 94, pp.iv , April [17] M.H. Er and B.C. Ng, A new approach to robust beamforming in the presence of steering vector errors, IEEE Trans. SP, pp , July [18] G.L. Fudge and D.A. Linebarger, A calibrated generalized sidelobe canceller for wideband beamforming, IEEE Trans. SP, pp , Oct [19] O. Hoshuyama and A. Sugiyama, A robust generalized sidelobe canceller with a blocking matrix using leaky adaptive filters, IEICE Trans., vol.j79-a, no.9, pp , Sept (English version is available in Electronics and Communications in Japan, vol.80, no.8, pp.56 65, Aug ) Appendix: Derivation of Eq. (11) The ideal interpolation function for a group delay τ in samples has an envelope as follows: sin π{n P τ} π{n P τ} 1. (A 1) π n P τ By scanning τ in Eq. (A 1) from T m to T m, Eq. (11) is obtained as the maximum value of right term in

8 HOSHUYAMA et al: A ROBUST ADAPTIVE BEAMFORMER USING COEFFICIENT-CONSTRAINED ADAPTIVE FILTERS 647 Eq. (A 1). In Eq. (11), 0.1 is only for avoiding divergence of φ m,n and ψ m,n. Osamu Hoshuyama received the B.Eng. and M.Eng. degrees in electrical engineering from of the University of Tokyo, Tokyo, Japan, in 1991 and 1993, respectively. He joined NEC Corporation, Kawasaki, Japan, in 1993 and has been engaged in researches on electro acoustics including adaptive filters and microphone arrays. He was awarded the 1997 Academic Encouragement Award by IEICE. Mr. Hoshuyama is a member of the Acoustical Society of Japan and IEEE. Akihiro Hirano received the B.Eng. and M.Eng. degrees in electronics engineering from Kanazawa University, Kanazawa, Japan in 1987 and 1989, respectively. He joined NEC Corporation, Kawasaski, Japan in 1989, where he had been a Research Engineer in Research and Development Group. Since 1998, he is a Research Associate at Faculty of Enginnering, Kanazawa University. He has been engaged in researches on digital signal processing, especially echo cancellers and digital signal processors. He was awarded the 1995 Academic Encouragement Award by IEICE. Mr. Hirano is a member of IEEE. Akihiko Sugiyama received the B.Eng., M.Eng., and Dr.Eng. degrees in electrical engineering from Tokyo Metropolitan University, Tokyo, Japan, in 1979, 1981, and 1998, respectively. He joined NEC Corporation, Kawasaki, Japan, in 1981 and has been engaged in researches on signal processor applications to transmission terminals, subscriber loop transmission systems, adaptive filter applications, and hifi audio coding. In the 1987 academic year, he was on leave at the Faculty of Engineering and Computer Science, Concordia University, Montreal, Canada, as a Visiting Scientist. From 1989 to 1994, he had been involved in the activities of the Audio Subgroup, ISO/IEC JTC1/SC29/WG11 (known as MPEG/Audio)for international standardization of high-quality audio data compression as a member of the Japanese delegation. His current interests lie in the area of signal processing and circuit theory. He served as an associate editor for the IEEE Transactions on Signal Processing from 1994 to He is also a member of the Technical Committee for Audio and Electroacoustics. He is currently serving as an associate editor for the Transactions of IEICE, Vol.A. He was awarded the 1988 Shinohara Memorial Academic Encouragement Award by the Institute of Electronics, Information and Communication Engineers of Japan. He is a coauthor of International Standards for Multimedia Coding, Maruzen Co. Ltd., 1991, and MPEG/International Standards for Multimedia Coding, Maruzen Co. Ltd., 1994, MPEG, Ohmusha Ltd., 1996, and Digital Broadcasting, Ohmusha Ltd., 1996.

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