# A BROADBAND BEAMFORMER USING CONTROLLABLE CONSTRAINTS AND MINIMUM VARIANCE

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2 2.1. Signal model 2. PROBLEM FORMULATION We consider a microphone array, consisting of M omnidirectional microphones, receives broadband signals from N acoustic sources besides a background noise, where N M. In general, we model the received signals at the frequency index f in a vector notation as y(f) = [ Y 1 (f) Y 2 (f) Y M (f) ] T, where Y m (f) is the mth microphone narrowband signal and the superscript T is the transpose operator. We write the vector y(f) as a function of the (known) steering vectors d n (f) and signal sources X n (f) for n = 1,..., N [4, 16] like N y(f) = d 1 (f)x 1 (f) + d n (f)x n (f) + v(f) n=2 = D(f)x(f) + v(f), (1) where v(f) = [ V 1 (f) V 2 (f) V M (f) ] T is the additive background noise, x(f) = [ X 1 (f) X 2 (f) X N (f) ] T is the collection of signal sources, and we define D(f) as the M N matrix containing all steering vectors relating to the N signal sources, i.e., D(f) = [ d 1 (f) d 2 (f) d N (f) ]. (2) We assume that X n (f) and V m (f) are uncorrelated and zero mean. Furthermore, we consider X 1 (f) as the desired signal that we wish to extract from the observations, while X n (f) for n = 2, 3,..., N are interferers. The correlation matrix of y(f) is defined as Φ y (f) = E [ y(f)y H (f) ], where E[ ] denotes mathematical expectation, and the superscript H is the transpose-conjugate operator. If we assume all signal sources and noise are uncorrelated, we can write the correlation matrix as Φ y (f) = D(f) Φ x (f) D H (f) + Φ v (f) = d 1 (f) d H 1 (f) + Φ in (f) + Φ v (f), (3) where Φ x (f) = diag[ φ X2 (f)... φ XN (f) ] is a diagonal matrix of size N N containing the variances of the sources at the frequency index f, i.e., φ Xn (f) = E [ X n (f) 2 ], the correlation matrix of v(f) is Φ v (f) = E [ v(f)v H (f) ], and Φ in (f) = N n=2 d n(f) φ Xn (f) d H n(f) is the interference correlation matrix. If the components of the steering vectors are only phase shifts, which is usually the case, then d H n(f)d n (f) = M. As a result, we can deduce the narrowband input SIR and input SINR respectively like isir(f) = isinr(f) = N n=2 φ X n (f), (4) M tr [Φ in (f) + Φ v (f)], (5) where tr [ ] denotes the trace of a square matrix. We apply a complex-valued filter, or a beamformer as we refer to, = [ H 1 (f) H 2 (f) H M (f) ] T on the microphone outputs, that results Z(f) = h H (f) y(f) with the variance of Φ Z (f) = h H (f) d 1 (f) d H 1 (f) + (6) h H (f) [ Φ in (f) + Φ v (f) ]. With the distortionless constraint that h H (f)d 1 (f) = 1, we can write the narrowband output SIR and output SINR respectively like osir[] = h H (f) Φ in (f), (7) osinr[] = h H (f) [ Φ in (f) + Φ v (f) ]. (8) 2.2. Minimum variance beamformers A fixed beamformer is a signal independent filter with a specific beampattern, e.g., the DS beamforg has a unit gain at the desired DOA, i.e., h DS (f) = d 1 (f)/m. However the desired signal is obtained from the desired direction, the output signal suffers from interference-plus-noise except for the unlikely cases when the nulls of the DS beamformer are situated at the direction of interferers. Signal dependent beamformers are designed adaptively to imize the variance of the output signal. The MVDR or the Capon method [11] imizes the output interference-plus-noise variance of the beamformer [2], i.e., h H (f) [ Φ in (f) + Φ v (f) ] (9) subject to h H (f) d 1 (f) = 1, and the MVDR beamformer is given by [4] h M (f) = [ Φ in (f) + Φ v (f) ] 1 d 1 (f) d H 1 (f) [ Φ in(f) + Φ v (f) ] 1 d 1 (f). (1) In the MVDR filter design, interferers are assumed to be uncorrelated with the desired signal; otherwise the desired signal may be suppressed. Herein, we generalize the MVDR beamformer to derive the LCMV filter that nulls out N 1 number of interferers and imizes the noise variance, i.e., h H (f) Φ v (f) (11) subject to h H (f) D(f) = i T N, where i N is the first column of the N N identity matrix, I N. The solution for the LCMV beamformer is h L (f) = Φ 1 v (f) D(f)[ D H (f) Φ 1 v (f) D(f) ] 1 i N. (12)

3 3. PROPOSED METHOD 2 2 The optimization procedures in the MVDR and the LCMV beamformers consist of the number of constraints and the residual (interference-plus-)noise. To design a beamformer which has properties between those beamformers, we now introduce a general expression for the signal model. We divide N signal sources into two sets of and N 2 sources as x(f) = [ x T (f) x T N 2 (f) ] T. Therefore, the received signals can be written like y(f) = D N1 (f) x N1 (f) + [ D N2 (f) x N2 (f) + v(f) ], (13) where D N1 (f) and D N2 (f) are matrices containing the steering vectors of the related signal sets, i.e., D(f) = [ D N1 (f) D N2 (f) ]. We can rewrite the correlation matrix of this decomposition as Φ y (f) = D N1 (f) Φ xn1 (f) D H (f) + Φ in,n2 (f) + Φ v (f), (14) where Φ in,n2 (f) = D N2 (f) Φ xn2 (f) D H N 2 (f), and Φ xn1 (f) and Φ xn2 (f) are the correlation matrices of the x T (f) and x T (f) signal sets, respectively. We apply the signal decomposition model (13) to propose a beamformer which we call the controllable LCMV (C- LCMV) inspired from LCMV and MVDR beamformers. For the set of signal sources, containing the desired signal, the filter is constrained to null out the remaining 1 interferers, and the remaining N 2 = N signal sources are imized together with the background noise, i.e., h H (f) [ Φ in,n2 (f) + Φ v (f) ] (15) subject to h H (f) D N1 (f) = i T. The C-LCMV beamformer is designed using the method of Lagrange multipliers as h C (f) = [ Φ in,n2 (f) + Φ v (f) ] 1 D N1 (f) (16) [ D H (f) [ Φ in,n2 (f) + Φ v (f) ] 1 D N1 (f) ] 1 i N1. This optimal filter is controlled using a different number of constraints, i.e. = 1, 2,..., N. In particular cases, if = 1 or = N, the filter will be the MVDR beamformer or the LCMV beamformer, respectively. Therefore, the C- LCMV beamformer has the following properties: osinr [h L (f)] osinr [h C (f)] osinr [h M (f)], (17) osir [h M (f)] osir [h C (f)] osir [h L (f)]. (18) 4. SIMULATION RESULTS We investigate the performance of the C-LCMV beamformer comparing with the DS, MVDR, and LCMV beamformers osinr [ db ] = 2 = 3 = f/f s osir [ db ] 15 1 = 4 5 = 2 = 3 MVDR LCMV C LCMV f/f s Fig. 1. Output SINR (left) and output SIR (right) of different beamformers versus frequency (input SINR = 8 db, and input SIR = 13 db). in an anechoic environment. We use a uniform linear array (ULA) which the distance between microphones is δ =.4 m, i.e., smaller than the half of the imum wavelength to avoid spatial aliasing, and the wave propagation speed is assumed c = 34 m/s. By selecting the first microphone as the reference microphone, the steering vector d n (f) = d (f, θ n ) can be written as a function of the DOA of the nth signal source, i.e., θ n, as d n (f) = [ 1 e j2πfτ cos θn e j2(m 1)πfτ cos θn ] T, (19) where j = 1, and τ = δ/c is the delay between two successive sensors at the zero angle. In Figure 1, we plot narrowband osinrs and osirs for various number of constraints, where M = 9, and N = 5 white Gaussian signal sources at θ 1 = π/6, θ 2 = π/2, θ 3 = 2π/3, θ 4 = 5π/6, and θ 5 = π. This figure illustrates that the C-LCMV beamformer performs in the range between the MVDR and LCMV beamformers. In the next experiments, we use three speech signals and white Gaussian noise, which are located at θ n (for n = 1, 2, 3, and 4), and synthesized according to the signal model (1). The desired speech signal is an utterance of Then, the sun shine, and interferers are utterances of Why were you away? and Somebody decides to break it!. The speech signals were sampled at f s = 8. khz during 1.28 sec. The desired speech signal is expected to be enhanced using the aforementioned filters in frequencies.1 4. khz, because the linear constrained beamformers may have a low output SNR at low frequencies [18]. We divide this multi-channel signal into 75% overlap frames with 256 samples, and transform them into frequency-domain using a 256-point discrete Fourier transform (DFT). Finally, the output signal of designed filters are transfered into time-domain using the inverse DFT. The imum output power beamformer is closely related to the imum variance beamformer with the distortionless constraint and the perfect signal match [21]. Therefore, the (interference-plus-)noise correlation matrix can be replaced by Φ y (f) in the filter designs (1), (12), and (16). We run

4 SINR [ db ] SINR [ db ] SIR [ db ] SIR [ db ] M input SNR [ db ] Input DS MVDR LCMV C-LCMV Fig. 2. Output SINR (top row) and output SIR (bottom row) of different beamformers versus number of microphones in 2 db noise (left column) and versus input SNR level using M = 9 (right column). simulations using different number of microphones and background noise levels. Since two interfering speech signals may have correlation with the desired signal, that is likely, in the C-LCMV beamformer we only null them out and imize the power of the uncorrelated interfering signal by choosing = 3. Figure 2 shows that the broadband osinr and osir of the C-LCMV beamformer performs in the range between the MVDR and LCMV beamformers. The expectation is estimated by time averaging, and the correlation matrix of the received signals, at a time instance t, is estimated as ˆΦ y,t (f) = 1 B B y t,b (f) yt,b(f), H (2) b=1 where y t,b (f) is the bth spectral amplitude estimate out of the last B estimates [22]. Moreover, the full rank correlation matrix can be guaranteed by choosing the buffer size as B M, and we choose B = 1. In practice, the correlation matrix estimate may has error due to the limited number of samples in low isnrs and the doant desired signal. Diagonal loading [14] is a solution for this problem, i.e., ˆΦ y (f) ˆΦ y (f) + γi M, that we choose γ = 1 4. In 5 db broadband isinr (2 db background noise), Figure 3 shows spectrograms of the noisy signal at the first microphone, the output signals of beamformers using M = 11 microphones. Although the LCMV beamformer outperforms the MVDR beamformer by removing interferers, the LCMV beamformer distort the speech signal at low frequencies. The Fig. 3. According to the order of plots from top to down: the spectrograms of the noisy signal at the first microphone, the output signals of the MVDR, LCMV, and C-LCMV beamformers. experiment results indicate that the C-LCMV beamformer removes interference tracks from the noisy signal without distorting the desired signal at low frequencies. 5. CONCLUSION The work presented in this paper has focused on signal enhancement in the presence of interference. The LCMV beamformer may have infinite output SIR, but have a lower output SNR than the MVDR beamformer. This problem is increased dramatically using a high number of constraints to remove interferers, especially at low frequencies and closely spaced interference [18]. We have proposed the C-LCMV beamformer being able to control the quality of the signal of interest, a trade-off between noise reduction and interference rejection.

5 6. REFERENCES [1] J. Benesty, J. Chen, Y. Huang, and I. Cohen, Noise Reduction in Speech Processing. Springer-Verlag, 29. [2] J. Chen, J. Benesty, Y. Huang, and S. Doclo, New insights into the noise reduction Wiener filter, IEEE Trans. Audio, Speech, and Language Process., vol. 14, pp , Jul. 26. [3] B. D. Van Veen and K. M. Buckley, Beamforg: a versatile approach to spatial filtering, IEEE ASSP Mag., vol. 5, pp. 4 24, Apr [4] J. Benesty, Y. Huang, and J. Chen, Microphone Array Signal Processing, vol. 1. Springer-Verlag, 28. [5] Y. Kaneda and J. Ohga, Adaptive microphone-array system for noise reduction, IEEE Trans. Acoust., Speech, Signal Process., vol. 34, no. 6, pp , [6] S. Doclo and M. Moonen, On the output SNR of the speech-distortion weighted multichannel Wiener filter, IEEE Signal Process. Lett., vol. 12, no. 12, pp , 25. [7] S. Applebaum and D. Chapman, Adaptive arrays with main beam constraints, IEEE Trans. Antennas Propag., vol. 24, no. 5, pp , [8] K. Buckley, Broad-band beamforg and the generalized sidelobe canceller, IEEE Trans. Acoust., Speech, Signal Process., vol. 34, pp , Oct [15] S. Boll, Suppression of acoustic noise in speech using spectral subtraction, IEEE Trans. Acoust., Speech, Signal Process., vol. 27, pp , Apr [16] J. Benesty, J. Chen, and E. A. P. Habets, Speech Enhancement in the STFT Domain, vol. 5. Springer, 212. [17] H. Steyskal, Wide-band nulling performance versus number of pattern constraints for an array antenna, IEEE Trans. Antennas Propag., vol. 31, pp , Jan [18] M. Souden, J. Benesty, and S. Affes, A study of the LCMV and MVDR noise reduction filters, IEEE Trans. Signal Process., vol. 58, pp , Sept. 21. [19] K. Bell, Y. Ephraim, and H. Van Trees, A bayesian approach to robust adaptive beamforg, IEEE Trans. Signal Process., vol. 48, pp , Feb 2. [2] H. Cox, R. Zeskind, and M. Owen, Robust adaptive beamforg, IEEE Trans. Acoust., Speech, Signal Process., vol. 35, pp , Oct [21] H. L. Van Trees, Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory. John Wiley & Sons, Inc., 22. [22] M. E. Lockwood and et al., Performance of timeand frequency-domain binaural beamformers based on recorded signals from real rooms, The Journal of the Acoustical Society of America, vol. 115, pp , Jan. 24. [9] S. Gannot, D. Burshtein, and E. Weinstein, Signal enhancement using beamforg and nonstationarity with applications to speech, IEEE Trans. Signal Process., vol. 49, no. 8, pp , 21. [1] H. Cox, R. Zeskind, and T. Kooij, Practical supergain, IEEE Trans. Acoust., Speech, Signal Process., vol. 34, no. 3, pp , [11] J. Capon, High-resolution frequency-wavenumber spectrum analysis, Proc. IEEE, vol. 57, pp , Aug [12] O. L. Frost, An algorithm for linearly constrained adaptive array processing, Proc. IEEE, vol. 6, pp , Aug [13] M. Brandstein and D. Ward, eds., Microphone Arrays - Signal Processing Techniques and Applications. Springer-Verlag, 21. [14] H. Cox, Resolving power and sensitivity to mismatch of optimum array processors, J. Acoust. Soc. Am., vol. 54, pp , Sep

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