Speech Enhancement Using Robust Generalized Sidelobe Canceller with Multi-Channel Post-Filtering in Adverse Environments

Size: px
Start display at page:

Download "Speech Enhancement Using Robust Generalized Sidelobe Canceller with Multi-Channel Post-Filtering in Adverse Environments"

Transcription

1 Chinese Journal of Electronics Vol.21, No.1, Jan Speech Enhancement Using Robust Generalized Sidelobe Canceller with Multi-Channel Post-Filtering in Adverse Environments LI Kai, FU Qiang and YAN Yonghong (Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing , China) Abstract In this paper, we propose a speech enhancement algorithm which has the feature of interaction between adaptive beamforming and multi-channel postfiltering. A novel subband feedback controller based on speech presence probability is applied to Generalized Sidelobe Canceller algorithm to obtain a more robust adaptive beamforming in adverse environment and alleviate the problem of signal cancellation. A multi-channel postfiltering is used not only to further suppress diffuse noises and some transient noises, but also to give the speech presence probability information in each subband. Experimental results show that the proposed algorithm achieves considerable improvement on signal preservation of the desired speech in adverse noise environments over the comparative algorithms. Key words Speech enhancement, Microphone array, Generalized sidelobe canceller, Adaptive filter, Postfiltering. I. Introduction Microphone array has been widely used to improve the performance of speech communication and Automatic speech recognition (ASR) systems in adverse noise environments because of their effectiveness in enhancing the quality of the captured speech [1,2]. Compared with single channel systems, a substantial gain in performance is obtainable due to the spatial filtering capability to suppress interfering signals coming from undesired directions. In practical environments, there are both directional noises which have some determinable directions (e.g. competitive speaker s voice or background music) and diffuse noises which come from all directions due to the diffuse reflections of the room. To suppress directional noises, a lot of algorithms based on beamformer have been proposed [1,2]. Van Veen and Buckley [3] classified various types of beamformers according to spatial filtering methods and analyzed their beam patterns. The Frost beamformer [4] was one of the first array structures to handle adaptive broad-band array processing. Griffiths and Jim [5] proposed an alternative method of Frost s algorithm and introduced the Generalized sidelobe canceller (GSC) solution, which not only effectively reduces the computational complexity but also provides flexibility to implement different beamformers. However, GSC algorithm suffers from signal cancellation problem because of the steering vector error, reverberation or imperfect microphones [1,6]. This problem has been noticed by some researchers and many adaptive beamforming algorithms have been proposed to avoid that [7 13]. Most of these methods, however, are not robust in transient non-stationary noise environment. In order to prevent the algorithms from diverging, several trials need to be conducted before a proper step-size is found. These drawbacks obviously will obstruct the use of these adaptive beamforming algorithms in practice. To suppress diffuse noises, post-filtering is normally needed. Zelinski s postfilter [14] employs auto- and cross- correlation functions of received multi-channel signals to derive a proper gain for enhancement. However, this method is based on the assumption of incoherent noise field which is seldom satisfied in practical environments. A generalized expression for Zelinski postfilter has been derived based on the a priori knowledge of noise field [15]. J. Li and Masato Akagi [16,17] proposed a hybrid post-filter with the assumption of a diffuse noise field. A modified Zelinski post-filter is applied to the high frequencies to suppress spatially uncorrelated noise and a single-channel wiener post-filter is applied to the low frequencies for cancellation of spatial correlated noise. However, as the aperture of the array decreases, correlation of noise becomes stronger, which makes the distinction between noise and desired speech weaker. And the post-filters mentioned above become unreliable. Another drawback of these post-filtering techniques is that highly non-stationary noise components can not dealt with well in real world applications [18]. To deal with the problems of the traditional algorithms mentioned above, in this paper, a robust GSC algorithm which has the feature of interaction between beamforming and multichannel post-filtering is proposed, as shown in Fig.1. The outputs of Fixed beamforming (FBF) and a modified Blocking matrix (BM), which uses more spatial information are analy- Manuscript Received Dec. 2010; Accepted June This work is partially supported by the National Natural Science Foundation of China (No , , , , , , )

2 86 Chinese Journal of Electronics 2012 Fig. 1. The framework of the proposed algorithm zed in the Short-time Fourier transform (STFT) domain and regrouped into auditory subbands according to the Bark scale, which mimics the auditory characteristics of human ears. And adaptive interference cancellation is performed in each subband. A multi-channel signal presence probability estimation based post-filter [18] is adopted to further enhance the output of the robust GSC, which is particularly advantageous in nonstationary noise environments. Besides, this method does not need the difference of correlation between speech and noise, making it more robust on small aperture arrays. A closed-loop controller uses feedback to control states of a dynamic system can keep the control error to a minimum and dynamically compensate for disturbances to the system [19]. In speech enhancement area, adaptive beamforming can be seen as a dynamic system which is adaptive to the adverse environment. Besides, speech signal is sparse in time-frequency domain, traditional GSC algorithm does not using these characteristics. Based on these considerations, we propose a novel subband feedback controller based on speech presence probability which is derived from the post-filtering to feedback control the adaptive interference canceller of GSC in each subband. We modified Cohen s multi-channel post-filtering so that signal presence probability in each auditory subband can be derived. The update of the filter coefficients is slowed down when the desired speech is present so that the proposed algorithm is more robust to array imperfection or reverberation, as the desired speech may leak into the reference channel. The interaction between the multi-channel processing and the postfiltering leads to better signal preservation thus improves the algorithm s overall performance. The remainder of the paper is organized as follows: a detail of the proposed speech enhancement algorithm is introduced in Section II. In Section III, we evaluate our algorithm and compare it with other methods. Conclusions are drawn in Section IV. II. Proposed Speech Enhancement Algorithm Consider a four-sensor microphone array in noisy environment, the observed signal on each microphone is composed of desired speech signal, directional noises arriving from determinable directions and diffuse noises propagating in all directions. The aim of our task is to reduce both directional and diffuse noises simultaneously while keeping the desired speech distortionless. To implement this idea, we construct a speech enhancement system, as shown in Fig.1, which consists of three main parts: robust generalized sidelobe canceller for directional noises suppression, multi-channel post-filtering for diffuse noises suppression, and the interaction of these two parts through a signal presence probability-based subband feedback controller, detailed in the following three subsections. 1. Robust generalized sidelobe canceller To suppress directional noises, we proposed a robust GSC algorithm which has three main parts: FBF, modified BM, and auditory subband adaptive interferences cancellation as shown in Fig.1. In the original GSC beamformers, the BM parts was implemented by subtracting between observed signals on adjacent sensors, which indicates that only limited spatial information was used. Comparatively, the modified BM considers the spatial information not only between adjacent sensors but also other sensor pairs, given by: Experiment demonstrates the effectiveness of this BM in Section III. Signal from the output of FBF and BM (denoted as y(n) and u m(n) respectively) are segmented into temporal frames and analyzed by STFT. y(n) ST F T Y (k, l), u m(n) ST F T U m(k, l) (1) in which l and k denote the index of temporal frames and frequency bins, respectively, m = 1, 2, M 1, M is the number of the microphones. We regroup the frequency domain signal of each frame into B groups according to Bark scale. The vectors of bins within the bth group are denoted as Y b (l) and U b m(l) respectively. Recall that our goal is to minimize the output power under a constraint on the response at the desired direction. Since the constraint is satisfied in the fixed beamformer, this is an unconstrained minimization similar to Widrow s classical Adaptive

3 Speech Enhancement Using Robust Generalized Sidelobe Canceller with Multi-Channel Post-Filtering in Adverse noise cancellation problem [20]. J b m(l) = E[ Y b (l) W b m(l)u b m(l) 2 ] (2) J b m(l) denotes the energy in bth band, and E( ) is the expectation operator. Minimizing J b m(l) leads to where W b m(l) opt = Φb U my (l) Φ b U mu m (l), if dj b m(l) dw b m(l) = 0 (3) Φ b U my (l) = E[U b m(l)(y b (l)) H ] (4) Φ b U mu m (l) = E[U b m(l)(u b m(l)) H ] (5) ( ) H is the Hermitian transpose operator. In order to track changes, we process the signals by segments. The following Unconstrained frequency domain normalized LMS (UFNLMS) algorithm is used. The adaptive interference canceller filter in each of the subband is updated by a modified UFNLMS with a different norm constraint. where W b m(l + 1) = W b m(l) + µ U b m(l)(y b (l)) H P b est(l) (6) M 1 Pest(l) b = αpest(l b 1) + (1 α) Xm(l) b 2 (7) m=1 For a standard UFNLMS algorithm, we should calculate P b est(l) using the power of the noise reference signals, but we find in experiment that the signal cancellation problem is serious if we update the weight during speech presence, so we usex b m(l) which is the frequency domain representation of input sensor signals, instead. The performance is improved due to the fact that the adaptation term becomes relatively small during speech presence. This can be seen as an implicit control of the adaptive filter. In order to precisely control the filter adaptation in Generalized sidelobe canceller, we proposed a method to use the signal presence probability derived from the post-filtering to feedback control the adaptive interference canceller of GSC in each subband, which will be detailed in Section II.3. As speech is concerned, the energy of desired signal mainly centralizes in low frequencies, so the signal in this area appears to be more colorful, while in higher frequencies, signal energy appears to be much weaker. So it is reasonable that non-uniform filter banks, instead of the uniform ones, should be used to make the low frequency bands narrower to proceed explicit analysis while in the high frequency bands, the bandwidth should be broader to contain more signal energy in order that the adaptive interference canceller may converge more smoothly. Functioning adaptive interferences canceller in a series of subbands can improve the system s SNR gain as well as enable it to deal with multiple interferences in different bands. This auditory subband method has been proved to be effective in our previous work [21]. 2. Multi-channel post-filtering The residual diffuse noises are further suppressed by a signal presence probability based multi-channel post-filtering [18], which uses a multi-channel soft signal detection based on the non-stationary of the signals and the transient power ratio between the beamformer primary output and its reference noise signals to estimate the speech presence probability and noise power spectral density and then an optimal gain function that minimizes the mean square error of the log-spectral amplitude is applied. The post-filtering estimates the Ephraim-Malah (EM) gain [23] : G EM (k, l) and SPP: P (k, l). And final gain for enhancement G(k, l) is reached by G(k, l) = (G EM (k, l)) P (k,l) 1 P (k,l) Gmin (8) where G min is the minimum gain allowed. G EM (k, l) is derived from single channel approach mainly and is able to reduce the stationary and quasi-stationary noises. And P (k, l), which suggests the probability of the desired speech exists in the corresponding time-frequency unit, is calculated by considering the ratio between the transient power of the GSC output Z(k, l) and the transient power of the BM output reference signal U m(k, l). A low ratio indicates a larger transient power in the reference channel, which means that an interfering source is probably present. In this case, a smaller P (k, l) is assigned. Thus the non-stationary noise in Z(k, l) will be further suppressed according to Eq.(8) because a small P (k, l) will make the final gain approach G min. The enhanced spectrum is given by Ŝ(k, l) = G(k, l) Z(k, l) (9) and the enhanced signal is obtained by taking the inverse Fourier transform of the enhanced spectrum using the phase of the original noisy spectrum. Finally, the standard overlapand-add method is used to obtain the enhanced signal. As mentioned in Section II.3, SPP in each auditory subband is needed for constraining filter updates. This can be achieved by averaging SPP of the time frequency units within the corresponding subbands. 3. Subband feedback controlled adaptive filters In practical implementations, the target speaker may not stay precisely at 0. Moreover, the desired speech will also leak into the reference channel due to echo and reverberation characteristics of the room. Furthermore, the position and frequency response of the microphones may not be as precise as expected, leading to imperfect cancellation of the desired speech in the reference channel. So the minimization of Jm(l) b in Eq.(2) does not necessarily lead to maximization of output SNR, instead, a certain proportion of speech signal will be canceled as a result. The leakage will also cause false fluctuations of filter coefficients. To improve the system s robustness against the adversities mentioned above, it is preferable that the updating rate of the adaptive filters should be controlled according to the presence of the desired speech. When the desired speech is present, update mentioned in Eq.(6) should be slowed down. The adaptation speed and steady state error of the adaptive filter are highly related to the step-size constant [24], but it is very hard to find the optimal step-size which guarantees the good performance in a general environment. So u in Eq.(6) must vary in different frequency bands and temporal frames.

4 88 Chinese Journal of Electronics 2012 We propose an time-varying step-size which is controlled by the speech presence probability in each subband which is derived from the post-filtering described in the last section. p b (l) = 1 N b i=i 1,i 2,,i Nb P (i, l) (10) in which N b is the number of frequency bins within the bth subband, i 1, i 2,, i Nb is the index of frequency bins within the bth subband. ( µ b (l) = (1 p b (l))µ = 1 1 P (i, l))µ (11) N b i=i 1,i 2,,i Nb p b (l) is the signal presence probability derived from the postfiltering in the last section, 0 < p b (l) < 1. A greater p b (l) indicates a high probability that the desired signal may exist in the bth subband during the lth frame. Thus a smaller µ b (l) is achieved according to Eq.(11), resulting in slow updates of the adaptive filters which preserves the speech components. And a small p b (l) means the desired signal is mostly absent. So the updates become fast enough to adapt to the changing nature of the interferences. III. Evaluations and Discussions 1. Experimental configuration The microphone array used in this work is composed of 4 omni-directional MEMS (Micro electrical mechanical system) microphone in broadside orientation. The distance between the microphones is set to be 5cm. The system is implemented under a sampling rate of 8kHz. Fig. 2. Configuration of experiments in a room environment The experiment was taken place in a 6m 5m 3m conference room with a reverberation time of 300ms as shown in Fig.2. Two interferences (a competing speaker and a gauss white noise source) are located in 90 and 45 of the array, respectively. The speech source is ten male and ten female TIMIT sentences. The multi-channel clean speech is generated by computer simulation in a virtual room [25] with the same size and reverberation time of the conference room in which the interferences are recorded, so that clean speech signal can be obtained for objective evaluations. And then we mix the two parts with different global SNR levels ( 6 6dB). All the sound sources are 1m away from the array. For comparison, the multi-channel noisy speech is processed with six methods listed below. (1) GSC algorithm in time domain (GSC-TD) [5]. (2) GSC algorithm in frequency domain (GSC-FD) [26]. (3) GSC-FD with modified Blocking matrix (GSC-FD*). (4) GSC-FD*with Subband-feedback-controlled adaptive filters (GSC-FD*-SFC). (5) Cohen s algorithm [22]. (6) Proposed algorithm. 2. Objective evaluation measures and results To evaluate the studied noise reduction methods for speech enhancement, three objective speech quality measures were used: Noise reduction (NR), Log-spectral distance (LSD) and Perceptual evaluation of subjective quality (PESQ). (1) Noise reduction (NR) [22]. This measure compares the noise level in the enhanced signal to the noise level recorded by the first microphone. It is designed to test the system s noise canceling ability during non-speech segments. NR = 1 l 10 log fore2 (12) L l L l Ŝ2 in which fore denotes the signal received by one of the microphones, and Ŝ is the signal estimates. L is the set of frames containing only noise, and L is its cardinality. (2) Log-spectral distance(lsd) [22], which can be expressed as LSD = 1 L 1 { N/2 1 [10 log AS(k, l) L N/2 + 1 l=0 k=0 10 log AŜ(k, l)]2 } 1/2 (13) where AS(k, l) = max( S(k, l) 2, δ) is the spectral power clipped such that log-spectrum dynamic range is confined to about 50dB (that is δ = 10 50/10 max k,l { X(k, l) 2 }). And N is the order of Fast Fourier transform (FFT). (3) Perceptual evaluation of subjective quality (PESQ). This measure is able to predict subjective quality with good correlation in a very wide range of conditions specified by the ITU-T as recommendation P.862 [27]. Note that a higher PESQ means the higher speech quality of the enhanced signal. Table 1. PESQ-MOS scores Input SNR(dB) Noisy GSC-TD GSC-FD GSC-FD* GSC-FD*-SFC Cohen s method Proposed method The experiment results in the real room acoustic conditions are shown in Fig.3. Compared with (1) (4) algorithms, our proposed algorithm shows considerable improvement in terms of noise reduction and LSD in various SNR conditions. Compared with Cohen s algorithm, although, the noise reduction performance is similar, our proposed algorithm shows better signal preservation. It also shows that adaptive beamforming using frequency-domain adaptive filter exhibits fast convergence behavior and better performance of nulling wideband interferences. We can also notice that the modified fixed BM

5 Speech Enhancement Using Robust Generalized Sidelobe Canceller with Multi-Channel Post-Filtering in Adverse using more spatial information gains some improvement. The subband feedback controlled method can alleviate the problem of signal cancellation in adaptive beamformer and has better desired signal preservation. To further demonstrate this point, PESQ-MOS is employed, as shown in Table 1. Fig. 3. Performance comparison in real room environment under different noise level among different algorithms: GSC-TD (+), GSC-FD (*), GSC-FD* ( ), GSC-FD- SFC ( ), Cohen s algorithm ( ) and Proposed algorithm ( ) 3. Discussions From the experimental results presented in the last section, the superiorities of the proposed noise reduction method to the other traditional methods are discussed in the following paragraphs. The proposed modified Block matrix outperforms traditional GSC Block matrix due to the fact that detailed in the following. In the original GSC beamformer, the BM parts was implemented by subtracting between observed signals on adjacent sensors, which indicates that only limited spatial information was used. Comparatively, the modified BM considers the spatial information not only between adjacent sensors but also other sensor pairs. The proposed method outperforms the GSC beamformer. The traditional GSC beamformer suffers from signal cancellation problem because of the steering vector error, reverberation or imperfect microphones. To overcome this problem, we propose a subband feedback controller based on speech presence probability which is derived from the post-filtering to feedback control the adaptive interference canceller of GSC in each subband. The update of the filter coefficients is slowed down when the desired speech is present so that the proposed algorithm is more robust to array imperfection or reverberation, as the desired speech may leak into the reference channel. This method leads to better signal preservation thus improves the algorithm s overall performance. Furthermore, the partitioning of the signals in subbands will effectively convert a wideband signal to a number of narrow-band signals, thus a more effective processing will become possible. Adaptive beamforming using the frequencydomain NLMS exhibits fast convergence behavior and better performance of nulling wideband interferences than using the NLMS, especially for the larger eigenvalue spread. Compared with Cohen s method (GSC with a multichannel post-filtering), we can see that although, noise reduction performance is similar because we use the similar speech presence probability based multi-channel post-filtering to overcome the diffuse noises and transient noises, the improvement of signal preservation is considerable by our subband feedback controlled method. As a result, the proposed speech enhancement method provides the highest performance among the studied speech enhancement algorithms under all experimental conditions, as shown in Fig.3 and Table 1. Considering that the speech presence probability used by subband feedback controller is obtained from the post-filtering, it does not increase much computational cost. This method can also be applied to other Adaptive noise cancellation or Acoustic echo cancellation applications which need carefully control of adaptive filter. IV. Conclusion A multi-channel speech enhancement algorithm is proposed. The algorithm consists of three parts: directional noise suppression, which is based on a robust Generalized sidelobe canceller with subband feedback controlled adaptive filters; diffuse noise suppression which is implemented by a multichannel post-filtering based on speech presence probability; and the interaction of adaptive beamforming and post-filtering through a subband feedback controller. Experimental results indicate that the subband feedback controller make the filter adaptation more robust and alleviate the problem of signal cancellation in adaptive beamformer. The proposed algorithm achieves considerable improvement on signal preservation of the desired speech in adverse noise environments over the comparative algorithms. References [1] J. Benesty, J. Chen and Y. Huang, Microphone Array Signal Processing, Berlin, Germany: Springer-Verlag, [2] M. Brandstein and D. Ward, Microphone Arrays: Signal Processing Techniques and Applications, Berlin: Springer-Verlag, [3] V. Veen and B.D. Buckley, Beamforming: a versatile approach

6 90 Chinese Journal of Electronics 2012 to spatial filtering, IEEE Signal Processing Magazine, Vol.5, pp.4 24, [4] O.L. Frost, An algorithm for linearly constrained adaptive array processing, Proceedings of the IEEE, Vol.60, No.8, pp , Aug [5] L.J. Griffths and C.W. Jim, An alternative approach to linearly constrained adaptive beamforming, IEEE Transactions on Antennas and Propagation, Vol.30, No.1, pp.27 34, Jan [6] B. Widrow, Signal cancellation phenomena in adaptive antennas: causes and cures, IEEE Transactions on Antennas and Propagation, Vol.30, No.3, pp , [7] J.E. Greenberg, Evaluation of an adaptive beamforming method for hearing aids, J. Acoust. Soc. Am., Vol.91, No.3, pp , [8] O. Hoshuyama, A. Sugiyama and A. Hirano, A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters, IEEE Transactions on Signal Processing, Vol.47, No.10, pp , [9] S. Gannot, D. Burshtein and E. Weinstein, Signal enhancement using beamforming and nonstationarity with applications to speech, IEEE Transactions on Signal Processing, Vol.49, No.8, pp , [10] W. Herbordt and W. Kellermann, Analysis of blocking matrix for generalized sidelobe cancellers for non-stationary broadband signals, IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, Florida, USA, Vol.4, pp.iv 4187, May [11] W.H. Neo and B. Farhang-Boroujeny, Robust microphone arrays using subband adaptive filters, IEE Proc.-Vis. Image Signal Process., Vol.149, No.1, pp.17 25, [12] E. Warsitz, A. Krueger and R. Haeb-Umbach, Speech enhancement with a new generalized eigenvector blocking matrix for application in a generalized sidelobe canceller,, IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, USA, pp.73 76, [13] A. Krueger, E. Warsitz and R. Haeb-Umbach, Speech enhancement with a GSC-like structure employing eigenvectorbased transfer function ratios estimation, IEEE Trans. on Audio, Speech, and Language Processing, Vol.19, pp , Jan [14] R. Zelinski, A microphone array with adaptive post-filtering for noise reduction in reverberant rooms, IEEE International Conference on Acoustics, Speech, and Signal Processing, New York, USA, Vol.5, pp , May [15] I.A. McCowan and H. Bourlard, Microphone array post-filter based on noise field coherence, IEEE Transactions on Speech and Audio Processing, Vol.11, No.6, pp , [16] J. Li and M. Akagi, A noise reduction system based on hybrid noise estimation technique and post-filtering in arbitrary noise environments, Speech Communication, Vol.48, No.2, pp , [17] J. Li and M. Akagi, A hybrid microphone array post-filter in a diffuse noise field, Applied Acoustics, Vol.69, No.2, pp , [18] I. Cohen, Multichannel post-filtering in nonstationary noise environments, IEEE Transactions on Signal Processing, Vol.52, No.5, pp , [19] J.G.F. Franklin and A. Emami-Naeini, Feedback Control of Dynamic Systems, Addison-Wesley, Reading, MA, [20] B. Widrow, Adaptive noise cancelling, principles and applications, Proceedings of the IEEE, Vol.63, pp , [21] H. Zhang, Q. Fu and Y. Yan, Speech enhancement using compact microphone array and applications in distant speech acquisition, Chinese Journal of Electronics, Vol.18, No.3, pp , July [22] I. Cohen, Analysis of two-channel generalized sidelobe canceller (GSC) with post-filtering, IEEE Transactions on Speech and Audio Processing, Vol.11, No.6, pp , [23] Y. Ephraim and D. Malah, Speech enhancement using a minimum mean-square error log-spectral amplitude estimator, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.33, No.2, pp , [24] A. Mader, H. Puder and G.U. Schmidt, Step-size control for acoustic echo cancellation filters- an overview, Signal Processing, Vol.80, pp , [25] J.B. Allen and D.A. Berkley, Image method for efficiently simulating small-room acoustics, J. Acoust. Soc. Am., Vol.65, No.4, pp , Apr [26] Y.H. Chen and H.D. Fang, Frequency-domain implementation of griffiths-jim adaptive beamformer, J. Acoust. Soc. Am., Vol.91, No.6, pp , [27] A.W. Rix, J.G. Beerends, M.P. Hollier and A.P. Hekstra, Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs, in IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.2, pp , LI Kai received the B.E. degree from Electronic Engineering Department of Wuhan University in Currently he is a Ph.D. candidate at the Institute of Acoustics, Chinese Academy of Sciences. His research interests include single and multi-channel speech enhancement, microphone array signal processing and distant-talking speech recognition. ( FU Qiang received the B.E. degree from the Xi an Technological Uninversity, Xi an, China, in 1994, the M.S. degree in electronic engineering from Chongqing University of Posts and Telecommunications, Chongqing, China, in 1997, and the Ph.D. degree in electronic engineering from Xidian University, Xi an, in In 2000, he was working as a Researcher in Motorola China Research Center (MSRC), Shanghai, China. From 2001 to 2002, he was working as a senior Research Associate in Center for Spoken Language Understanding (CSLU), OGI School of Science and Engineering at Oregon Health & Science University, Oregon, USA. From 2002 to 2004, he was working as a Senior Postdoctoral Research Fellow in Department of Electric and Computer Engineering, University of Limerick, Ireland. He is currently an Associated Professor in Institute of Acoustics, Chinese Academy of Sciences, China. His research interests include speech analysis, microphone array processing and audio-visual signal processing, etc. Dr. Fu is a member of IEEE Signal Processing Society. YAN Yonghong received the B.E. degree from the Electronic Engineering Department of Tsinghua University in 1990, and Ph.D. degree in Computer Science and Engineering from Oregon Graduate Institute of Science and Engineering in From 1995 to 1998, he worked in OGI as an Assistant Professor, Associate Director and Associate Professor of the Center for Spoken Language Understanding. From 1998 to 2001 he worked as the Principal Engineer of Intel Microprocessors Research Lab, Director and Chief Scientist of Intel China Research Center. In 2002 he returned to China to work for Chinese Academy of Sciences. He is a professor and director of Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences. His research interests include large vocabulary speech recognition, speaker/language recognition and audio signal processing. He has published more than 100 papers and holds 40 patents.

Speech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya 2, B. Yamuna 2, H. Divya 2, B. Shiva Kumar 2, B.

Speech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya 2, B. Yamuna 2, H. Divya 2, B. Shiva Kumar 2, B. www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 4 April 2015, Page No. 11143-11147 Speech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya

More information

LETTER Pre-Filtering Algorithm for Dual-Microphone Generalized Sidelobe Canceller Using General Transfer Function

LETTER Pre-Filtering Algorithm for Dual-Microphone Generalized Sidelobe Canceller Using General Transfer Function IEICE TRANS. INF. & SYST., VOL.E97 D, NO.9 SEPTEMBER 2014 2533 LETTER Pre-Filtering Algorithm for Dual-Microphone Generalized Sidelobe Canceller Using General Transfer Function Jinsoo PARK, Wooil KIM,

More information

Emanuël A. P. Habets, Jacob Benesty, and Patrick A. Naylor. Presented by Amir Kiperwas

Emanuël A. P. Habets, Jacob Benesty, and Patrick A. Naylor. Presented by Amir Kiperwas Emanuël A. P. Habets, Jacob Benesty, and Patrick A. Naylor Presented by Amir Kiperwas 1 M-element microphone array One desired source One undesired source Ambient noise field Signals: Broadband Mutually

More information

IN REVERBERANT and noisy environments, multi-channel

IN REVERBERANT and noisy environments, multi-channel 684 IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 11, NO. 6, NOVEMBER 2003 Analysis of Two-Channel Generalized Sidelobe Canceller (GSC) With Post-Filtering Israel Cohen, Senior Member, IEEE Abstract

More information

Speech and Audio Processing Recognition and Audio Effects Part 3: Beamforming

Speech and Audio Processing Recognition and Audio Effects Part 3: Beamforming Speech and Audio Processing Recognition and Audio Effects Part 3: Beamforming Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Electrical Engineering and Information Engineering

More information

MULTICHANNEL systems are often used for

MULTICHANNEL systems are often used for IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 52, NO. 5, MAY 2004 1149 Multichannel Post-Filtering in Nonstationary Noise Environments Israel Cohen, Senior Member, IEEE Abstract In this paper, we present

More information

AN ADAPTIVE MICROPHONE ARRAY FOR OPTIMUM BEAMFORMING AND NOISE REDUCTION

AN ADAPTIVE MICROPHONE ARRAY FOR OPTIMUM BEAMFORMING AND NOISE REDUCTION AN ADAPTIVE MICROPHONE ARRAY FOR OPTIMUM BEAMFORMING AND NOISE REDUCTION Gerhard Doblinger Institute of Communications and Radio-Frequency Engineering Vienna University of Technology Gusshausstr. 5/39,

More information

AN ADAPTIVE MICROPHONE ARRAY FOR OPTIMUM BEAMFORMING AND NOISE REDUCTION

AN ADAPTIVE MICROPHONE ARRAY FOR OPTIMUM BEAMFORMING AND NOISE REDUCTION 1th European Signal Processing Conference (EUSIPCO ), Florence, Italy, September -,, copyright by EURASIP AN ADAPTIVE MICROPHONE ARRAY FOR OPTIMUM BEAMFORMING AND NOISE REDUCTION Gerhard Doblinger Institute

More information

Speech Enhancement Using Multi-channel Post-Filtering with Modified Signal Presence Probability in Reverberant Environment

Speech Enhancement Using Multi-channel Post-Filtering with Modified Signal Presence Probability in Reverberant Environment Chinese Journal of Electronics Vol.25, No.3, May 2016 Speech Enhancement Using Multi-channel Post-Filtering with Modified Signal Presence Probability in Reverberant Environment WANG Xiaofei, GUO Yanmeng,

More information

Adaptive beamforming using pipelined transform domain filters

Adaptive beamforming using pipelined transform domain filters Adaptive beamforming using pipelined transform domain filters GEORGE-OTHON GLENTIS Technological Education Institute of Crete, Branch at Chania, Department of Electronics, 3, Romanou Str, Chalepa, 73133

More information

ROBUST echo cancellation requires a method for adjusting

ROBUST echo cancellation requires a method for adjusting 1030 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 15, NO. 3, MARCH 2007 On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk Jean-Marc Valin, Member,

More information

A BROADBAND BEAMFORMER USING CONTROLLABLE CONSTRAINTS AND MINIMUM VARIANCE

A BROADBAND BEAMFORMER USING CONTROLLABLE CONSTRAINTS AND MINIMUM VARIANCE A BROADBAND BEAMFORMER USING CONTROLLABLE CONSTRAINTS AND MINIMUM VARIANCE Sam Karimian-Azari, Jacob Benesty,, Jesper Rindom Jensen, and Mads Græsbøll Christensen Audio Analysis Lab, AD:MT, Aalborg University,

More information

Towards an intelligent binaural spee enhancement system by integrating me signal extraction. Author(s)Chau, Duc Thanh; Li, Junfeng; Akagi,

Towards an intelligent binaural spee enhancement system by integrating me signal extraction. Author(s)Chau, Duc Thanh; Li, Junfeng; Akagi, JAIST Reposi https://dspace.j Title Towards an intelligent binaural spee enhancement system by integrating me signal extraction Author(s)Chau, Duc Thanh; Li, Junfeng; Akagi, Citation 2011 International

More information

Design and Implementation on a Sub-band based Acoustic Echo Cancellation Approach

Design and Implementation on a Sub-band based Acoustic Echo Cancellation Approach Vol., No. 6, 0 Design and Implementation on a Sub-band based Acoustic Echo Cancellation Approach Zhixin Chen ILX Lightwave Corporation Bozeman, Montana, USA chen.zhixin.mt@gmail.com Abstract This paper

More information

Recent Advances in Acoustic Signal Extraction and Dereverberation

Recent Advances in Acoustic Signal Extraction and Dereverberation Recent Advances in Acoustic Signal Extraction and Dereverberation Emanuël Habets Erlangen Colloquium 2016 Scenario Spatial Filtering Estimated Desired Signal Undesired sound components: Sensor noise Competing

More information

Microphone Array Feedback Suppression. for Indoor Room Acoustics

Microphone Array Feedback Suppression. for Indoor Room Acoustics Microphone Array Feedback Suppression for Indoor Room Acoustics by Tanmay Prakash Advisor: Dr. Jeffrey Krolik Department of Electrical and Computer Engineering Duke University 1 Abstract The objective

More information

Speech Enhancement in Presence of Noise using Spectral Subtraction and Wiener Filter

Speech Enhancement in Presence of Noise using Spectral Subtraction and Wiener Filter Speech Enhancement in Presence of Noise using Spectral Subtraction and Wiener Filter 1 Gupteswar Sahu, 2 D. Arun Kumar, 3 M. Bala Krishna and 4 Jami Venkata Suman Assistant Professor, Department of ECE,

More information

Study Of Sound Source Localization Using Music Method In Real Acoustic Environment

Study Of Sound Source Localization Using Music Method In Real Acoustic Environment International Journal of Electronics Engineering Research. ISSN 975-645 Volume 9, Number 4 (27) pp. 545-556 Research India Publications http://www.ripublication.com Study Of Sound Source Localization Using

More information

arxiv: v1 [cs.sd] 4 Dec 2018

arxiv: v1 [cs.sd] 4 Dec 2018 LOCALIZATION AND TRACKING OF AN ACOUSTIC SOURCE USING A DIAGONAL UNLOADING BEAMFORMING AND A KALMAN FILTER Daniele Salvati, Carlo Drioli, Gian Luca Foresti Department of Mathematics, Computer Science and

More information

Michael Brandstein Darren Ward (Eds.) Microphone Arrays. Signal Processing Techniques and Applications. With 149 Figures. Springer

Michael Brandstein Darren Ward (Eds.) Microphone Arrays. Signal Processing Techniques and Applications. With 149 Figures. Springer Michael Brandstein Darren Ward (Eds.) Microphone Arrays Signal Processing Techniques and Applications With 149 Figures Springer Contents Part I. Speech Enhancement 1 Constant Directivity Beamforming Darren

More information

Broadband Microphone Arrays for Speech Acquisition

Broadband Microphone Arrays for Speech Acquisition Broadband Microphone Arrays for Speech Acquisition Darren B. Ward Acoustics and Speech Research Dept. Bell Labs, Lucent Technologies Murray Hill, NJ 07974, USA Robert C. Williamson Dept. of Engineering,

More information

Speech Enhancement Based On Noise Reduction

Speech Enhancement Based On Noise Reduction Speech Enhancement Based On Noise Reduction Kundan Kumar Singh Electrical Engineering Department University Of Rochester ksingh11@z.rochester.edu ABSTRACT This paper addresses the problem of signal distortion

More information

Comparison of LMS and NLMS algorithm with the using of 4 Linear Microphone Array for Speech Enhancement

Comparison of LMS and NLMS algorithm with the using of 4 Linear Microphone Array for Speech Enhancement Comparison of LMS and NLMS algorithm with the using of 4 Linear Microphone Array for Speech Enhancement Mamun Ahmed, Nasimul Hyder Maruf Bhuyan Abstract In this paper, we have presented the design, implementation

More information

Smart antenna for doa using music and esprit

Smart antenna for doa using music and esprit IOSR Journal of Electronics and Communication Engineering (IOSRJECE) ISSN : 2278-2834 Volume 1, Issue 1 (May-June 2012), PP 12-17 Smart antenna for doa using music and esprit SURAYA MUBEEN 1, DR.A.M.PRASAD

More information

Title. Author(s)Sugiyama, Akihiko; Kato, Masanori; Serizawa, Masahir. Issue Date Doc URL. Type. Note. File Information

Title. Author(s)Sugiyama, Akihiko; Kato, Masanori; Serizawa, Masahir. Issue Date Doc URL. Type. Note. File Information Title A Low-Distortion Noise Canceller with an SNR-Modifie Author(s)Sugiyama, Akihiko; Kato, Masanori; Serizawa, Masahir Proceedings : APSIPA ASC 9 : Asia-Pacific Signal Citationand Conference: -5 Issue

More information

PAPER Adaptive Microphone Array System with Two-Stage Adaptation Mode Controller

PAPER Adaptive Microphone Array System with Two-Stage Adaptation Mode Controller 972 IEICE TRANS. FUNDAMENTALS, VOL.E88 A, NO.4 APRIL 2005 PAPER Adaptive Microphone Array System with Two-Stage Adaptation Mode Controller Yang-Won JUNG a), Student Member, Hong-Goo KANG, Chungyong LEE,

More information

Subspace Noise Estimation and Gamma Distribution Based Microphone Array Post-filter Design

Subspace Noise Estimation and Gamma Distribution Based Microphone Array Post-filter Design Chinese Journal of Electronics Vol.0, No., Apr. 011 Subspace Noise Estimation and Gamma Distribution Based Microphone Array Post-filter Design CHENG Ning 1,,LIUWenju 3 and WANG Lan 1, (1.Shenzhen Institutes

More information

IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 21, NO. 5, MAY

IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 21, NO. 5, MAY IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 21, NO. 5, MAY 2013 945 A Two-Stage Beamforming Approach for Noise Reduction Dereverberation Emanuël A. P. Habets, Senior Member, IEEE,

More information

Reduction of Musical Residual Noise Using Harmonic- Adapted-Median Filter

Reduction of Musical Residual Noise Using Harmonic- Adapted-Median Filter Reduction of Musical Residual Noise Using Harmonic- Adapted-Median Filter Ching-Ta Lu, Kun-Fu Tseng 2, Chih-Tsung Chen 2 Department of Information Communication, Asia University, Taichung, Taiwan, ROC

More information

A COHERENCE-BASED ALGORITHM FOR NOISE REDUCTION IN DUAL-MICROPHONE APPLICATIONS

A COHERENCE-BASED ALGORITHM FOR NOISE REDUCTION IN DUAL-MICROPHONE APPLICATIONS 18th European Signal Processing Conference (EUSIPCO-21) Aalborg, Denmark, August 23-27, 21 A COHERENCE-BASED ALGORITHM FOR NOISE REDUCTION IN DUAL-MICROPHONE APPLICATIONS Nima Yousefian, Kostas Kokkinakis

More information

Dual Transfer Function GSC and Application to Joint Noise Reduction and Acoustic Echo Cancellation

Dual Transfer Function GSC and Application to Joint Noise Reduction and Acoustic Echo Cancellation Dual Transfer Function GSC and Application to Joint Noise Reduction and Acoustic Echo Cancellation Gal Reuven Under supervision of Sharon Gannot 1 and Israel Cohen 2 1 School of Engineering, Bar-Ilan University,

More information

546 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 4, MAY /$ IEEE

546 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 4, MAY /$ IEEE 546 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL 17, NO 4, MAY 2009 Relative Transfer Function Identification Using Convolutive Transfer Function Approximation Ronen Talmon, Israel

More information

Different Approaches of Spectral Subtraction Method for Speech Enhancement

Different Approaches of Spectral Subtraction Method for Speech Enhancement ISSN 2249 5460 Available online at www.internationalejournals.com International ejournals International Journal of Mathematical Sciences, Technology and Humanities 95 (2013 1056 1062 Different Approaches

More information

The Hybrid Simplified Kalman Filter for Adaptive Feedback Cancellation

The Hybrid Simplified Kalman Filter for Adaptive Feedback Cancellation The Hybrid Simplified Kalman Filter for Adaptive Feedback Cancellation Felix Albu Department of ETEE Valahia University of Targoviste Targoviste, Romania felix.albu@valahia.ro Linh T.T. Tran, Sven Nordholm

More information

Airo Interantional Research Journal September, 2013 Volume II, ISSN:

Airo Interantional Research Journal September, 2013 Volume II, ISSN: Airo Interantional Research Journal September, 2013 Volume II, ISSN: 2320-3714 Name of author- Navin Kumar Research scholar Department of Electronics BR Ambedkar Bihar University Muzaffarpur ABSTRACT Direction

More information

OPTIMUM POST-FILTER ESTIMATION FOR NOISE REDUCTION IN MULTICHANNEL SPEECH PROCESSING

OPTIMUM POST-FILTER ESTIMATION FOR NOISE REDUCTION IN MULTICHANNEL SPEECH PROCESSING 14th European Signal Processing Conference (EUSIPCO 6), Florence, Italy, September 4-8, 6, copyright by EURASIP OPTIMUM POST-FILTER ESTIMATION FOR NOISE REDUCTION IN MULTICHANNEL SPEECH PROCESSING Stamatis

More information

Eigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction

Eigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction Short Course @ISAP2010 in MACAO Eigenvalues and Eigenvectors in Array Antennas Optimization of Array Antennas for High Performance Nobuyoshi Kikuma Nagoya Institute of Technology, Japan 1 Self-introduction

More information

Automotive three-microphone voice activity detector and noise-canceller

Automotive three-microphone voice activity detector and noise-canceller Res. Lett. Inf. Math. Sci., 005, Vol. 7, pp 47-55 47 Available online at http://iims.massey.ac.nz/research/letters/ Automotive three-microphone voice activity detector and noise-canceller Z. QI and T.J.MOIR

More information

Robust Low-Resource Sound Localization in Correlated Noise

Robust Low-Resource Sound Localization in Correlated Noise INTERSPEECH 2014 Robust Low-Resource Sound Localization in Correlated Noise Lorin Netsch, Jacek Stachurski Texas Instruments, Inc. netsch@ti.com, jacek@ti.com Abstract In this paper we address the problem

More information

NOISE REDUCTION IN DUAL-MICROPHONE MOBILE PHONES USING A BANK OF PRE-MEASURED TARGET-CANCELLATION FILTERS. P.O.Box 18, Prague 8, Czech Republic

NOISE REDUCTION IN DUAL-MICROPHONE MOBILE PHONES USING A BANK OF PRE-MEASURED TARGET-CANCELLATION FILTERS. P.O.Box 18, Prague 8, Czech Republic NOISE REDUCTION IN DUAL-MICROPHONE MOBILE PHONES USING A BANK OF PRE-MEASURED TARGET-CANCELLATION FILTERS Zbyněk Koldovský 1,2, Petr Tichavský 2, and David Botka 1 1 Faculty of Mechatronic and Interdisciplinary

More information

NOISE REDUCTION IN DUAL-MICROPHONE MOBILE PHONES USING A BANK OF PRE-MEASURED TARGET-CANCELLATION FILTERS. P.O.Box 18, Prague 8, Czech Republic

NOISE REDUCTION IN DUAL-MICROPHONE MOBILE PHONES USING A BANK OF PRE-MEASURED TARGET-CANCELLATION FILTERS. P.O.Box 18, Prague 8, Czech Republic NOISE REDUCTION IN DUAL-MICROPHONE MOBILE PHONES USING A BANK OF PRE-MEASURED TARGET-CANCELLATION FILTERS Zbyněk Koldovský 1,2, Petr Tichavský 2, and David Botka 1 1 Faculty of Mechatronic and Interdisciplinary

More information

Residual noise Control for Coherence Based Dual Microphone Speech Enhancement

Residual noise Control for Coherence Based Dual Microphone Speech Enhancement 008 International Conference on Computer and Electrical Engineering Residual noise Control for Coherence Based Dual Microphone Speech Enhancement Behzad Zamani Mohsen Rahmani Ahmad Akbari Islamic Azad

More information

Speech enhancement with a GSC-like structure employing sparse coding

Speech enhancement with a GSC-like structure employing sparse coding 1154 Yang et al. / J Zhejiang Univ-Sci C (Comput & Electron) 2014 15(12):1154-1163 Journal of Zhejiang University-SCIENCE C (Computers & Electronics) ISSN 1869-1951 (Print); ISSN 1869-196X (Online) www.zju.edu.cn/jzus;

More information

RECENTLY, there has been an increasing interest in noisy

RECENTLY, there has been an increasing interest in noisy IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 52, NO. 9, SEPTEMBER 2005 535 Warped Discrete Cosine Transform-Based Noisy Speech Enhancement Joon-Hyuk Chang, Member, IEEE Abstract In

More information

Introduction to distributed speech enhancement algorithms for ad hoc microphone arrays and wireless acoustic sensor networks

Introduction to distributed speech enhancement algorithms for ad hoc microphone arrays and wireless acoustic sensor networks Introduction to distributed speech enhancement algorithms for ad hoc microphone arrays and wireless acoustic sensor networks Part I: Array Processing in Acoustic Environments Sharon Gannot 1 and Alexander

More information

Adaptive Noise Reduction Algorithm for Speech Enhancement

Adaptive Noise Reduction Algorithm for Speech Enhancement Adaptive Noise Reduction Algorithm for Speech Enhancement M. Kalamani, S. Valarmathy, M. Krishnamoorthi Abstract In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to

More information

Acoustic Beamforming for Hearing Aids Using Multi Microphone Array by Designing Graphical User Interface

Acoustic Beamforming for Hearing Aids Using Multi Microphone Array by Designing Graphical User Interface MEE-2010-2012 Acoustic Beamforming for Hearing Aids Using Multi Microphone Array by Designing Graphical User Interface Master s Thesis S S V SUMANTH KOTTA BULLI KOTESWARARAO KOMMINENI This thesis is presented

More information

NOISE ESTIMATION IN A SINGLE CHANNEL

NOISE ESTIMATION IN A SINGLE CHANNEL SPEECH ENHANCEMENT FOR CROSS-TALK INTERFERENCE by Levent M. Arslan and John H.L. Hansen Robust Speech Processing Laboratory Department of Electrical Engineering Box 99 Duke University Durham, North Carolina

More information

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion American Journal of Applied Sciences 5 (4): 30-37, 008 ISSN 1546-939 008 Science Publications A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion Zayed M. Ramadan

More information

Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis

Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Mohini Avatade & S.L. Sahare Electronics & Telecommunication Department, Cummins

More information

/$ IEEE

/$ IEEE IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 6, AUGUST 2009 1071 Multichannel Eigenspace Beamforming in a Reverberant Noisy Environment With Multiple Interfering Speech Signals

More information

Analysis of LMS Algorithm in Wavelet Domain

Analysis of LMS Algorithm in Wavelet Domain Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013) Analysis of LMS Algorithm in Wavelet Domain Pankaj Goel l, ECE Department, Birla Institute of Technology Ranchi, Jharkhand,

More information

TRANSIENT NOISE REDUCTION BASED ON SPEECH RECONSTRUCTION

TRANSIENT NOISE REDUCTION BASED ON SPEECH RECONSTRUCTION TRANSIENT NOISE REDUCTION BASED ON SPEECH RECONSTRUCTION Jian Li 1,2, Shiwei Wang 1,2, Renhua Peng 1,2, Chengshi Zheng 1,2, Xiaodong Li 1,2 1. Communication Acoustics Laboratory, Institute of Acoustics,

More information

Auditory System For a Mobile Robot

Auditory System For a Mobile Robot Auditory System For a Mobile Robot PhD Thesis Jean-Marc Valin Department of Electrical Engineering and Computer Engineering Université de Sherbrooke, Québec, Canada Jean-Marc.Valin@USherbrooke.ca Motivations

More information

A MULTI-CHANNEL POSTFILTER BASED ON THE DIFFUSE NOISE SOUND FIELD. Lukas Pfeifenberger 1 and Franz Pernkopf 1

A MULTI-CHANNEL POSTFILTER BASED ON THE DIFFUSE NOISE SOUND FIELD. Lukas Pfeifenberger 1 and Franz Pernkopf 1 A MULTI-CHANNEL POSTFILTER BASED ON THE DIFFUSE NOISE SOUND FIELD Lukas Pfeifenberger 1 and Franz Pernkopf 1 1 Signal Processing and Speech Communication Laboratory Graz University of Technology, Graz,

More information

IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 5, NO. 5, SEPTEMBER

IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 5, NO. 5, SEPTEMBER IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 5, NO. 5, SEPTEMBER 1997 425 A Signal Subspace Tracking Algorithm for Microphone Array Processing of Speech Sofiène Affes, Member, IEEE, and Yves

More information

INTERFERENCE REJECTION OF ADAPTIVE ARRAY ANTENNAS BY USING LMS AND SMI ALGORITHMS

INTERFERENCE REJECTION OF ADAPTIVE ARRAY ANTENNAS BY USING LMS AND SMI ALGORITHMS INTERFERENCE REJECTION OF ADAPTIVE ARRAY ANTENNAS BY USING LMS AND SMI ALGORITHMS Kerim Guney Bilal Babayigit Ali Akdagli e-mail: kguney@erciyes.edu.tr e-mail: bilalb@erciyes.edu.tr e-mail: akdagli@erciyes.edu.tr

More information

Effective post-processing for single-channel frequency-domain speech enhancement Weifeng Li a

Effective post-processing for single-channel frequency-domain speech enhancement Weifeng Li a R E S E A R C H R E P O R T I D I A P Effective post-processing for single-channel frequency-domain speech enhancement Weifeng Li a IDIAP RR 7-7 January 8 submitted for publication a IDIAP Research Institute,

More information

Antennas and Propagation. Chapter 5c: Array Signal Processing and Parametric Estimation Techniques

Antennas and Propagation. Chapter 5c: Array Signal Processing and Parametric Estimation Techniques Antennas and Propagation : Array Signal Processing and Parametric Estimation Techniques Introduction Time-domain Signal Processing Fourier spectral analysis Identify important frequency-content of signal

More information

Analysis of LMS and NLMS Adaptive Beamforming Algorithms

Analysis of LMS and NLMS Adaptive Beamforming Algorithms Analysis of LMS and NLMS Adaptive Beamforming Algorithms PG Student.Minal. A. Nemade Dept. of Electronics Engg. Asst. Professor D. G. Ganage Dept. of E&TC Engg. Professor & Head M. B. Mali Dept. of E&TC

More information

DISTANT or hands-free audio acquisition is required in

DISTANT or hands-free audio acquisition is required in 158 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 1, JANUARY 2010 New Insights Into the MVDR Beamformer in Room Acoustics E. A. P. Habets, Member, IEEE, J. Benesty, Senior Member,

More information

High-speed Noise Cancellation with Microphone Array

High-speed Noise Cancellation with Microphone Array Noise Cancellation a Posteriori Probability, Maximum Criteria Independent Component Analysis High-speed Noise Cancellation with Microphone Array We propose the use of a microphone array based on independent

More information

Direction-of-Arrival Estimation Using a Microphone Array with the Multichannel Cross-Correlation Method

Direction-of-Arrival Estimation Using a Microphone Array with the Multichannel Cross-Correlation Method Direction-of-Arrival Estimation Using a Microphone Array with the Multichannel Cross-Correlation Method Udo Klein, Member, IEEE, and TrInh Qu6c VO School of Electrical Engineering, International University,

More information

Published in: Proceedings of the 11th International Workshop on Acoustic Echo and Noise Control

Published in: Proceedings of the 11th International Workshop on Acoustic Echo and Noise Control Aalborg Universitet Variable Speech Distortion Weighted Multichannel Wiener Filter based on Soft Output Voice Activity Detection for Noise Reduction in Hearing Aids Ngo, Kim; Spriet, Ann; Moonen, Marc;

More information

Signal Processing 91 (2011) Contents lists available at ScienceDirect. Signal Processing. journal homepage:

Signal Processing 91 (2011) Contents lists available at ScienceDirect. Signal Processing. journal homepage: Signal Processing 9 (2) 55 6 Contents lists available at ScienceDirect Signal Processing journal homepage: www.elsevier.com/locate/sigpro Fast communication Minima-controlled speech presence uncertainty

More information

Improving Meetings with Microphone Array Algorithms. Ivan Tashev Microsoft Research

Improving Meetings with Microphone Array Algorithms. Ivan Tashev Microsoft Research Improving Meetings with Microphone Array Algorithms Ivan Tashev Microsoft Research Why microphone arrays? They ensure better sound quality: less noises and reverberation Provide speaker position using

More information

Performance study of Text-independent Speaker identification system using MFCC & IMFCC for Telephone and Microphone Speeches

Performance study of Text-independent Speaker identification system using MFCC & IMFCC for Telephone and Microphone Speeches Performance study of Text-independent Speaker identification system using & I for Telephone and Microphone Speeches Ruchi Chaudhary, National Technical Research Organization Abstract: A state-of-the-art

More information

NOISE reduction, sometimes also referred to as speech enhancement,

NOISE reduction, sometimes also referred to as speech enhancement, 2034 IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 22, NO. 12, DECEMBER 2014 A Family of Maximum SNR Filters for Noise Reduction Gongping Huang, Student Member, IEEE, Jacob Benesty,

More information

Microphone Array Design and Beamforming

Microphone Array Design and Beamforming Microphone Array Design and Beamforming Heinrich Löllmann Multimedia Communications and Signal Processing heinrich.loellmann@fau.de with contributions from Vladi Tourbabin and Hendrik Barfuss EUSIPCO Tutorial

More information

Students: Avihay Barazany Royi Levy Supervisor: Kuti Avargel In Association with: Zoran, Haifa

Students: Avihay Barazany Royi Levy Supervisor: Kuti Avargel In Association with: Zoran, Haifa Students: Avihay Barazany Royi Levy Supervisor: Kuti Avargel In Association with: Zoran, Haifa Spring 2008 Introduction Problem Formulation Possible Solutions Proposed Algorithm Experimental Results Conclusions

More information

Mel Spectrum Analysis of Speech Recognition using Single Microphone

Mel Spectrum Analysis of Speech Recognition using Single Microphone International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree

More information

Audio Restoration Based on DSP Tools

Audio Restoration Based on DSP Tools Audio Restoration Based on DSP Tools EECS 451 Final Project Report Nan Wu School of Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI, United States wunan@umich.edu Abstract

More information

Single Channel Speaker Segregation using Sinusoidal Residual Modeling

Single Channel Speaker Segregation using Sinusoidal Residual Modeling NCC 2009, January 16-18, IIT Guwahati 294 Single Channel Speaker Segregation using Sinusoidal Residual Modeling Rajesh M Hegde and A. Srinivas Dept. of Electrical Engineering Indian Institute of Technology

More information

Joint dereverberation and residual echo suppression of speech signals in noisy environments Habets, E.A.P.; Gannot, S.; Cohen, I.; Sommen, P.C.W.

Joint dereverberation and residual echo suppression of speech signals in noisy environments Habets, E.A.P.; Gannot, S.; Cohen, I.; Sommen, P.C.W. Joint dereverberation and residual echo suppression of speech signals in noisy environments Habets, E.A.P.; Gannot, S.; Cohen, I.; Sommen, P.C.W. Published in: IEEE Transactions on Audio, Speech, and Language

More information

SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN. Yu Wang and Mike Brookes

SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN. Yu Wang and Mike Brookes SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN Yu Wang and Mike Brookes Department of Electrical and Electronic Engineering, Exhibition Road, Imperial College London,

More information

Implementation of Optimized Proportionate Adaptive Algorithm for Acoustic Echo Cancellation in Speech Signals

Implementation of Optimized Proportionate Adaptive Algorithm for Acoustic Echo Cancellation in Speech Signals International Journal of Electronics Engineering Research. ISSN 0975-6450 Volume 9, Number 6 (2017) pp. 823-830 Research India Publications http://www.ripublication.com Implementation of Optimized Proportionate

More information

Robust Voice Activity Detection Based on Discrete Wavelet. Transform

Robust Voice Activity Detection Based on Discrete Wavelet. Transform Robust Voice Activity Detection Based on Discrete Wavelet Transform Kun-Ching Wang Department of Information Technology & Communication Shin Chien University kunching@mail.kh.usc.edu.tw Abstract This paper

More information

Li, Junfeng; Sakamoto, Shuichi; Hong Author(s) Akagi, Masato; Suzuki, Yôiti. Citation Speech Communication, 53(5):

Li, Junfeng; Sakamoto, Shuichi; Hong Author(s) Akagi, Masato; Suzuki, Yôiti. Citation Speech Communication, 53(5): JAIST Reposi https://dspace.j Title Two-stage binaural speech enhancemen filter for high-quality speech commu Li, Junfeng; Sakamoto, Shuichi; Hong Author(s) Akagi, Masato; Suzuki, Yôiti Citation Speech

More information

Performance improvement in beamforming of Smart Antenna by using LMS algorithm

Performance improvement in beamforming of Smart Antenna by using LMS algorithm Performance improvement in beamforming of Smart Antenna by using LMS algorithm B. G. Hogade Jyoti Chougale-Patil Shrikant K.Bodhe Research scholar, Student, ME(ELX), Principal, SVKM S NMIMS,. Terna Engineering

More information

NOISE POWER SPECTRAL DENSITY MATRIX ESTIMATION BASED ON MODIFIED IMCRA. Qipeng Gong, Benoit Champagne and Peter Kabal

NOISE POWER SPECTRAL DENSITY MATRIX ESTIMATION BASED ON MODIFIED IMCRA. Qipeng Gong, Benoit Champagne and Peter Kabal NOISE POWER SPECTRAL DENSITY MATRIX ESTIMATION BASED ON MODIFIED IMCRA Qipeng Gong, Benoit Champagne and Peter Kabal Department of Electrical & Computer Engineering, McGill University 3480 University St.,

More information

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

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

More information

RASTA-PLP SPEECH ANALYSIS. Aruna Bayya. Phil Kohn y TR December 1991

RASTA-PLP SPEECH ANALYSIS. Aruna Bayya. Phil Kohn y TR December 1991 RASTA-PLP SPEECH ANALYSIS Hynek Hermansky Nelson Morgan y Aruna Bayya Phil Kohn y TR-91-069 December 1991 Abstract Most speech parameter estimation techniques are easily inuenced by the frequency response

More information

A Comparison of the Convolutive Model and Real Recording for Using in Acoustic Echo Cancellation

A Comparison of the Convolutive Model and Real Recording for Using in Acoustic Echo Cancellation A Comparison of the Convolutive Model and Real Recording for Using in Acoustic Echo Cancellation SEPTIMIU MISCHIE Faculty of Electronics and Telecommunications Politehnica University of Timisoara Vasile

More information

Performance Study of A Non-Blind Algorithm for Smart Antenna System

Performance Study of A Non-Blind Algorithm for Smart Antenna System International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 5, Number 4 (2012), pp. 447-455 International Research Publication House http://www.irphouse.com Performance Study

More information

SPEECH ENHANCEMENT WITH SIGNAL SUBSPACE FILTER BASED ON PERCEPTUAL POST FILTERING

SPEECH ENHANCEMENT WITH SIGNAL SUBSPACE FILTER BASED ON PERCEPTUAL POST FILTERING SPEECH ENHANCEMENT WITH SIGNAL SUBSPACE FILTER BASED ON PERCEPTUAL POST FILTERING K.Ramalakshmi Assistant Professor, Dept of CSE Sri Ramakrishna Institute of Technology, Coimbatore R.N.Devendra Kumar Assistant

More information

Speech Enhancement for Nonstationary Noise Environments

Speech Enhancement for Nonstationary Noise Environments Signal & Image Processing : An International Journal (SIPIJ) Vol., No.4, December Speech Enhancement for Nonstationary Noise Environments Sandhya Hawaldar and Manasi Dixit Department of Electronics, KIT

More information

Adaptive Beamforming Applied for Signals Estimated with MUSIC Algorithm

Adaptive Beamforming Applied for Signals Estimated with MUSIC Algorithm Buletinul Ştiinţific al Universităţii "Politehnica" din Timişoara Seria ELECTRONICĂ şi TELECOMUNICAŢII TRANSACTIONS on ELECTRONICS and COMMUNICATIONS Tom 57(71), Fascicola 2, 2012 Adaptive Beamforming

More information

Mikko Myllymäki and Tuomas Virtanen

Mikko Myllymäki and Tuomas Virtanen NON-STATIONARY NOISE MODEL COMPENSATION IN VOICE ACTIVITY DETECTION Mikko Myllymäki and Tuomas Virtanen Department of Signal Processing, Tampere University of Technology Korkeakoulunkatu 1, 3370, Tampere,

More information

Adaptive Speech Enhancement Using Partial Differential Equations and Back Propagation Neural Networks

Adaptive Speech Enhancement Using Partial Differential Equations and Back Propagation Neural Networks Australian Journal of Basic and Applied Sciences, 4(7): 2093-2098, 2010 ISSN 1991-8178 Adaptive Speech Enhancement Using Partial Differential Equations and Back Propagation Neural Networks 1 Mojtaba Bandarabadi,

More information

PATH UNCERTAINTY ROBUST BEAMFORMING. Richard Stanton and Mike Brookes. Imperial College London {rs408,

PATH UNCERTAINTY ROBUST BEAMFORMING. Richard Stanton and Mike Brookes. Imperial College London {rs408, PATH UNCERTAINTY ROBUST BEAMFORMING Richard Stanton and Mike Brookes Imperial College London {rs8, mike.brookes}@imperial.ac.uk ABSTRACT Conventional beamformer design assumes that the phase differences

More information

inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE

inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE Copyright SFA - InterNoise 2000 1 inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering 27-30 August 2000, Nice, FRANCE I-INCE Classification: 7.2 MICROPHONE ARRAY

More information

ZLS38500 Firmware for Handsfree Car Kits

ZLS38500 Firmware for Handsfree Car Kits Firmware for Handsfree Car Kits Features Selectable Acoustic and Line Cancellers (AEC & LEC) Programmable echo tail cancellation length from 8 to 256 ms Reduction - up to 20 db for white noise and up to

More information

Blind Dereverberation of Single-Channel Speech Signals Using an ICA-Based Generative Model

Blind Dereverberation of Single-Channel Speech Signals Using an ICA-Based Generative Model Blind Dereverberation of Single-Channel Speech Signals Using an ICA-Based Generative Model Jong-Hwan Lee 1, Sang-Hoon Oh 2, and Soo-Young Lee 3 1 Brain Science Research Center and Department of Electrial

More information

Multiple Antenna Processing for WiMAX

Multiple Antenna Processing for WiMAX Multiple Antenna Processing for WiMAX Overview Wireless operators face a myriad of obstacles, but fundamental to the performance of any system are the propagation characteristics that restrict delivery

More information

260 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 2, FEBRUARY /$ IEEE

260 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 2, FEBRUARY /$ IEEE 260 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 2, FEBRUARY 2010 On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction Mehrez Souden, Student Member,

More information

Wavelet Speech Enhancement based on the Teager Energy Operator

Wavelet Speech Enhancement based on the Teager Energy Operator Wavelet Speech Enhancement based on the Teager Energy Operator Mohammed Bahoura and Jean Rouat ERMETIS, DSA, Université du Québec à Chicoutimi, Chicoutimi, Québec, G7H 2B1, Canada. Abstract We propose

More information

ACOUSTIC feedback problems may occur in audio systems

ACOUSTIC feedback problems may occur in audio systems IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL 20, NO 9, NOVEMBER 2012 2549 Novel Acoustic Feedback Cancellation Approaches in Hearing Aid Applications Using Probe Noise and Probe Noise

More information

THE PROBLEM of electromagnetic interference between

THE PROBLEM of electromagnetic interference between IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL. 50, NO. 2, MAY 2008 399 Estimation of Current Distribution on Multilayer Printed Circuit Board by Near-Field Measurement Qiang Chen, Member, IEEE,

More information

Design of Robust Differential Microphone Arrays

Design of Robust Differential Microphone Arrays IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 22, NO. 10, OCTOBER 2014 1455 Design of Robust Differential Microphone Arrays Liheng Zhao, Jacob Benesty, Jingdong Chen, Senior Member,

More information

REAL-TIME BROADBAND NOISE REDUCTION

REAL-TIME BROADBAND NOISE REDUCTION REAL-TIME BROADBAND NOISE REDUCTION Robert Hoeldrich and Markus Lorber Institute of Electronic Music Graz Jakoministrasse 3-5, A-8010 Graz, Austria email: robert.hoeldrich@mhsg.ac.at Abstract A real-time

More information