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Yiteng Huang - One of the best experts on this subject based on the ideXlab platform.

  • Study of the widely linear Wiener Filter for noise reduction
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    This paper develops a new widely linear noise-reduction Wiener Filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction Filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener Filter; 2) its minimum mean-squared error (MSE) is smaller than that of the classical Wiener Filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener Filter has no effect on the subband SNR for any given signal frame and subband.

  • Analysis of the frequency-domain Wiener Filter with the prediction gain
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jingdong Chen, Jacob Benesty, Yiteng Huang
    Abstract:

    This paper presents a theoretical analysis on the performance of the optimal noise-reduction Filter in the frequency domain. Using the autoregressive (AR) model to model both the clean speech and noise, we build the relationship between the Wiener Filter and the AR parameters of the clean speech and noise signals. We show that if noise is not predictable, the Wiener Filter is mostly related to the AR parameters of the desired speech signal. On the contrary, if the desired signal is not predictable, the Wiener Filter is then mostly related to the AR parameters of the noise signal. More importantly, we provide the bounds for noise reduction, speech distortion, and SNR improvement, and show that the performance of the Wiener Filter in terms of SNR improvement and degree of noise reduction and speech distortion is closely related to the prediction gain of the desired speech and noise signals.

  • ICASSP - Study of the widely linear Wiener Filter for noise reduction
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    This paper develops a new widely linear noise-reduction Wiener Filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction Filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener Filter; 2) its minimum mean-squared error (MSE) is smaller than that of the classical Wiener Filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener Filter has no effect on the subband SNR for any given signal frame and subband.

  • New insights into the noise reduction Wiener Filter
    IEEE Transactions on Audio Speech and Language Processing, 2006
    Co-Authors: Jingdong Chen, Jacob Benesty, Yiteng Huang, S. Doclo
    Abstract:

    The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener Filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener Filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener Filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener Filter) is greater than or equal to the a priori SNR (defined before the Wiener Filter), indicating that the Wiener Filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener Filter, resulting in a suboptimal Wiener Filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

  • study of the Wiener Filter for noise reduction
    2005
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang, S. Doclo
    Abstract:

    The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Numerous techniques were developed, and among them is the optimal Wiener Filter, which is the most fundamental approach, and has been delineated in different forms and adopted in diversified applications. It is not a secret that the Wiener Filter achieves noise reduction with some integrity loss of the speech signal. However, few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index and a noise-reduction factor, this chapter studies the quantitative performance behavior of the Wiener Filter in the context of noise reduction. We show that for a single-channel Wiener Filter, the amount of noise attenuation is in general proportionate to the amount of speech degradation. In other words, the more the noise is reduced, the more the speech is distorted. This may seem discouraging as we always expect an algorithm to have maximal noise attenuation without much speech distortion. Fortunately, we show that the speech distortion can be better managed by properly manipulating the Wiener Filter, or by considering some knowledge of the speech signal. The former leads to a sub-optimal Wiener Filter where a parameter is introduced to control the tradeoff between speech distortion and noise reduction, and the latter leads to the well-known parametric-model-based noise reduction technique. We also show that speech distortion can even be avoided if we have multiple realizations of the speech signal.

Jingdong Chen - One of the best experts on this subject based on the ideXlab platform.

  • Study of the widely linear Wiener Filter for noise reduction
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    This paper develops a new widely linear noise-reduction Wiener Filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction Filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener Filter; 2) its minimum mean-squared error (MSE) is smaller than that of the classical Wiener Filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener Filter has no effect on the subband SNR for any given signal frame and subband.

  • Analysis of the frequency-domain Wiener Filter with the prediction gain
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jingdong Chen, Jacob Benesty, Yiteng Huang
    Abstract:

    This paper presents a theoretical analysis on the performance of the optimal noise-reduction Filter in the frequency domain. Using the autoregressive (AR) model to model both the clean speech and noise, we build the relationship between the Wiener Filter and the AR parameters of the clean speech and noise signals. We show that if noise is not predictable, the Wiener Filter is mostly related to the AR parameters of the desired speech signal. On the contrary, if the desired signal is not predictable, the Wiener Filter is then mostly related to the AR parameters of the noise signal. More importantly, we provide the bounds for noise reduction, speech distortion, and SNR improvement, and show that the performance of the Wiener Filter in terms of SNR improvement and degree of noise reduction and speech distortion is closely related to the prediction gain of the desired speech and noise signals.

  • ICASSP - Study of the widely linear Wiener Filter for noise reduction
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    This paper develops a new widely linear noise-reduction Wiener Filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction Filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener Filter; 2) its minimum mean-squared error (MSE) is smaller than that of the classical Wiener Filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener Filter has no effect on the subband SNR for any given signal frame and subband.

  • New insights into the noise reduction Wiener Filter
    IEEE Transactions on Audio Speech and Language Processing, 2006
    Co-Authors: Jingdong Chen, Jacob Benesty, Yiteng Huang, S. Doclo
    Abstract:

    The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener Filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener Filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener Filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener Filter) is greater than or equal to the a priori SNR (defined before the Wiener Filter), indicating that the Wiener Filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener Filter, resulting in a suboptimal Wiener Filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

  • study of the Wiener Filter for noise reduction
    2005
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang, S. Doclo
    Abstract:

    The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Numerous techniques were developed, and among them is the optimal Wiener Filter, which is the most fundamental approach, and has been delineated in different forms and adopted in diversified applications. It is not a secret that the Wiener Filter achieves noise reduction with some integrity loss of the speech signal. However, few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index and a noise-reduction factor, this chapter studies the quantitative performance behavior of the Wiener Filter in the context of noise reduction. We show that for a single-channel Wiener Filter, the amount of noise attenuation is in general proportionate to the amount of speech degradation. In other words, the more the noise is reduced, the more the speech is distorted. This may seem discouraging as we always expect an algorithm to have maximal noise attenuation without much speech distortion. Fortunately, we show that the speech distortion can be better managed by properly manipulating the Wiener Filter, or by considering some knowledge of the speech signal. The former leads to a sub-optimal Wiener Filter where a parameter is introduced to control the tradeoff between speech distortion and noise reduction, and the latter leads to the well-known parametric-model-based noise reduction technique. We also show that speech distortion can even be avoided if we have multiple realizations of the speech signal.

Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.

  • On the Identification of Bilinear Forms With the Wiener Filter
    IEEE Signal Processing Letters, 2017
    Co-Authors: Jacob Benesty, Constantin Paleologu, Silviu Ciochina
    Abstract:

    In this letter, the identification problem of bilinear forms with the Wiener Filter is addressed. The contribution is twofold. First, a different approach is introduced, by defining the bilinear term with respect to the impulse responses of a spatiotemporal model, in the context of multiple-input/single-output systems. Second, two versions of the Wiener Filter (namely direct and iterative) are developed in this context. Moreover, the advantage of the iterative Wiener Filter is outlined as compared to the direct solution. The results of the simulations, which are performed from a system identification perspective, support the theoretical findings.

  • Study of the widely linear Wiener Filter for noise reduction
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    This paper develops a new widely linear noise-reduction Wiener Filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction Filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener Filter; 2) its minimum mean-squared error (MSE) is smaller than that of the classical Wiener Filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener Filter has no effect on the subband SNR for any given signal frame and subband.

  • Analysis of the frequency-domain Wiener Filter with the prediction gain
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jingdong Chen, Jacob Benesty, Yiteng Huang
    Abstract:

    This paper presents a theoretical analysis on the performance of the optimal noise-reduction Filter in the frequency domain. Using the autoregressive (AR) model to model both the clean speech and noise, we build the relationship between the Wiener Filter and the AR parameters of the clean speech and noise signals. We show that if noise is not predictable, the Wiener Filter is mostly related to the AR parameters of the desired speech signal. On the contrary, if the desired signal is not predictable, the Wiener Filter is then mostly related to the AR parameters of the noise signal. More importantly, we provide the bounds for noise reduction, speech distortion, and SNR improvement, and show that the performance of the Wiener Filter in terms of SNR improvement and degree of noise reduction and speech distortion is closely related to the prediction gain of the desired speech and noise signals.

  • ICASSP - Study of the widely linear Wiener Filter for noise reduction
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    This paper develops a new widely linear noise-reduction Wiener Filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction Filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener Filter; 2) its minimum mean-squared error (MSE) is smaller than that of the classical Wiener Filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener Filter has no effect on the subband SNR for any given signal frame and subband.

  • New insights into the noise reduction Wiener Filter
    IEEE Transactions on Audio Speech and Language Processing, 2006
    Co-Authors: Jingdong Chen, Jacob Benesty, Yiteng Huang, S. Doclo
    Abstract:

    The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener Filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener Filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener Filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener Filter) is greater than or equal to the a priori SNR (defined before the Wiener Filter), indicating that the Wiener Filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener Filter, resulting in a suboptimal Wiener Filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

Yukihiko Yamashita - One of the best experts on this subject based on the ideXlab platform.

  • Pattern recognition by kernel Wiener Filter
    2008
    Co-Authors: Hirokazu Yoshino, Yukihiko Yamashita
    Abstract:

    Wiener Filter is used widely for the inverse problem. From an observed signal, it provides the best restored signal with respect to the squared error averaged over the original signal and the noise among linear operators. In this paper, we propose applying the kernel Wiener Filter, which enables us to handle signals non-linearly by mapping signals to the high dimensional space with kernel trick, to the pattern recognition problem. We regard a pattern as an observed signal and provide an identical original vector for the patterns belonging to the same class. Finally we classify an unknown pattern into the class of which vector is the nearest to the restored signal in the high dimensional space of the original space. In addition, we apply linear approximation to the kernel function to enable the regularization based on the distance in the observed signal space to enhance its performance of generalization. And also we adjust the value of the kernel function in the high dimensional original signal space to improve its ability further more.

  • Non-linear Wiener Filter in reproducing kernel Hilbert space
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Y. Washizawa, Yukihiko Yamashita
    Abstract:

    Wiener Filters are used widely for inverse problems. From an observed signal, a Wiener Filter provides the best restored signal with respect to the square error averaged over the original signal and the noise among linear operators. We introduce the non-linear Wiener Filter, which is a kernel-based extension of the Wiener Filter. When the kernel method is applied to the Wiener Filter directly, the dimensions of the space where the calculation has to be done is very large since noise samples have to be used. We provide a realistic solution using the first order approximation. Moreover, we provide the experimental results to demonstrate the advantages of this method

W. Utschick - One of the best experts on this subject based on the ideXlab platform.

  • Reduced complexity transmit Wiener Filter based on a Krylov subspace multi-stage decomposition
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: J. Brehmer, M. Joham, G. Dietl, W. Utschick
    Abstract:

    The multi-stage transmit Wiener Filter (MSTxWF) is presented, an approach to reducing the complexity of the transmit Wiener Filter (TxWF). The MSTxWF is found by applying the multi-stage decomposition known from the receive multi-stage Wiener Filter (MSWF) to the TxWF. Complexity reduction is achieved by truncating the decomposition. We show that the resulting reduced rank MSTxWF can be interpreted as an approximation of the TxWF in a Krylov subspace, allowing for an efficient computation of the MSTxWF with the Lanczos algorithm. The reduced rank MSTxWF shows near-optimum performance for relatively low rank, making it an interesting alternative to eigenspace-based methods for complexity reduction.

  • Transmit Wiener Filter for the downlink of TDDDS-CDMA systems
    IEEE Seventh International Symposium on Spread Spectrum Techniques and Applications, 2002
    Co-Authors: M. Joham, K. Kusume, M.h. Gzara, W. Utschick, J.a. Nossek
    Abstract:

    We derive the transmit Wiener Filter for DS-CDMA systems which depends upon the noise power at the receivers. We show that the transmit Wiener Filter converges to the transmit matched Filter and the transmit zero-forcing Filter for low and high signal-to-noise-ratio, respectively. Simulation results show the superiority of the transmit Wiener Filter compared to the other two transmit Filters. Moreover, we observe that the application of the three transmit Filters and the respective receive Filters lead to similar results.