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Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
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a single Channel Noise reduction filtering smoothing technique in the time domain
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Ningning Pan, Jacob Benesty, Jingdong ChenAbstract:In this paper, we present a single-Channel smoothing-and-filtering technique for Noise reduction in the time domain. Unlike traditional Noise reduction methods, which directly apply a Noise reduction filter to the noisy signal, the developed technique achieves Noise reduction in two steps. It first applies a time smoothing window to the noisy signal, which, on the one hand, can help reduce high frequency Noise and, on the other hand, can help leverage the correlation between successive signal samples. A Noise reduction filter is then applied to the smoothed noisy signal to estimate the speech signal of interest. Three optimal and suboptimal Noise reduction filters are derived, including the Wiener, maximum signal-to-Noise-ratio (SNR), and tradeoff filters. Simulation results reveal that the developed method can produce better Noise reduction performance, i.e., higher gains in the perceptual-evaluation-of-speech-quality (PESQ) score, than the traditional methods without smoothing.
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optimal single Channel Noise reduction filtering matrices from the pearson correlation coefficient perspective
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-Channel Noise reduction in the time domain, where an estimate of a vector of the desired clean speech is achieved by filtering a frame of the noisy signal with a rectangular filtering matrix. The core issue with this problem formulation is then the estimation of the optimal filtering matrix. The squared Pearson correlation coefficient (SPCC) is used. We show that different optimal filtering matrices can be derived by maximizing or minimizing the SPCCs between different signals. For example, maximizing the SPCC between the enhanced signal and the filtered speech gives the reduced-rankWiener and minimum distortion (MD) filtering matrices while minimizing the SPCC gives the minimum Noise (MN) and another reduced-rank Wiener filtering matrices. Simulation results are presented to illustrate the properties of these filtering matrices.
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Single-Channel Noise reduction using unified joint diagonalization and optimal filtering
EURASIP Journal on Advances in Signal Processing, 2014Co-Authors: Sidsel Marie Nørholm, Jacob Benesty, Jesper Rindom Jensen, Mads Græsbøll ChristensenAbstract:In this paper, the important problem of single-Channel Noise reduction is treated from a new perspective. The problem is posed as a filtering problem based on joint diagonalization of the covariance matrices of the desired and Noise signals. More specifically, the eigenvectors from the joint diagonalization corresponding to the least significant eigenvalues are used to form a filter, which effectively estimates the Noise when applied to the observed signal. This estimate is then subtracted from the observed signal to form an estimate of the desired signal, i.e., the speech signal. In doing this, we consider two cases, where, respectively, no distortion and distortion are incurred on the desired signal. The former can be achieved when the covariance matrix of the desired signal is rank deficient, which is the case, for example, for voiced speech. In the latter case, the covariance matrix of the desired signal is full rank, as is the case, for example, in unvoiced speech. Here, the amount of distortion incurred is controlled via a simple, integer parameter, and the more distortion allowed, the higher the output signal-to-Noise ratio (SNR). Simulations demonstrate the properties of the two solutions. In the distortionless case, the proposed filter achieves only a slightly worse output SNR, compared to the Wiener filter, along with no signal distortion. Moreover, when distortion is allowed, it is possible to achieve higher output SNRs compared to the Wiener filter. Alternatively, when a lower output SNR is accepted, a filter with less signal distortion than the Wiener filter can be constructed.
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a multi frame approach to the frequency domain single Channel Noise reduction problem
IEEE Transactions on Audio Speech and Language Processing, 2012Co-Authors: Yiteng Arden Huang, Jacob BenestyAbstract:This paper focuses on the class of single-Channel Noise reduction methods that are performed in the frequency domain via the short-time Fourier transform (STFT). The simplicity and relative effectiveness of this class of approaches make them the dominant choice in practical systems. Over the past years, many popular algorithms have been proposed. These algorithms, no matter how they are developed, have one feature in common: the solution is eventually formulated as a gain function applied to the STFT of the noisy signal only in the current frame, implying that the interframe correlation is ignored. This assumption is not accurate for speech enhancement since speech is a highly self-correlated signal. In this paper, by taking the interframe correlation into account, a new linear model for speech spectral estimation and some optimal filters are proposed. They include the multi-frame Wiener and minimum variance distortionless response (MVDR) filters. With these filters, both the narrowband and fullband signal-to-Noise ratios (SNRs) can be improved. Furthermore, with the MVDR filter, speech distortion at the output can be zero. Simulations present promising results in support of the claimed merits obtained by theoretical analysis.
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single Channel Noise reduction with a rectangular filtering matrix
2011Co-Authors: Jacob Benesty, Jingdong ChenAbstract:In the previous chapter, we tried to estimate one sample only at a time from the observation signal vector. In this part, we are going to estimate more than one sample at a time. As a result, we now deal with a rectangular filtering matrix instead of a filtering vector. If M is the number of samples to be estimated and L is the length of the observation signal vector, then the size of the filtering matrix is M × L. Also, this approach is more general and all the results from Chap. 2 are particular cases of the results derived in this chapter by just setting M = 1. The signal model is the same as in Sect. 2.1; so we start by explaining the principle of linear filtering with a rectangular matrix.
Jingdong Chen - One of the best experts on this subject based on the ideXlab platform.
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a single Channel Noise reduction filtering smoothing technique in the time domain
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Ningning Pan, Jacob Benesty, Jingdong ChenAbstract:In this paper, we present a single-Channel smoothing-and-filtering technique for Noise reduction in the time domain. Unlike traditional Noise reduction methods, which directly apply a Noise reduction filter to the noisy signal, the developed technique achieves Noise reduction in two steps. It first applies a time smoothing window to the noisy signal, which, on the one hand, can help reduce high frequency Noise and, on the other hand, can help leverage the correlation between successive signal samples. A Noise reduction filter is then applied to the smoothed noisy signal to estimate the speech signal of interest. Three optimal and suboptimal Noise reduction filters are derived, including the Wiener, maximum signal-to-Noise-ratio (SNR), and tradeoff filters. Simulation results reveal that the developed method can produce better Noise reduction performance, i.e., higher gains in the perceptual-evaluation-of-speech-quality (PESQ) score, than the traditional methods without smoothing.
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optimal single Channel Noise reduction filtering matrices from the pearson correlation coefficient perspective
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-Channel Noise reduction in the time domain, where an estimate of a vector of the desired clean speech is achieved by filtering a frame of the noisy signal with a rectangular filtering matrix. The core issue with this problem formulation is then the estimation of the optimal filtering matrix. The squared Pearson correlation coefficient (SPCC) is used. We show that different optimal filtering matrices can be derived by maximizing or minimizing the SPCCs between different signals. For example, maximizing the SPCC between the enhanced signal and the filtered speech gives the reduced-rankWiener and minimum distortion (MD) filtering matrices while minimizing the SPCC gives the minimum Noise (MN) and another reduced-rank Wiener filtering matrices. Simulation results are presented to illustrate the properties of these filtering matrices.
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single Channel Noise reduction with a rectangular filtering matrix
2011Co-Authors: Jacob Benesty, Jingdong ChenAbstract:In the previous chapter, we tried to estimate one sample only at a time from the observation signal vector. In this part, we are going to estimate more than one sample at a time. As a result, we now deal with a rectangular filtering matrix instead of a filtering vector. If M is the number of samples to be estimated and L is the length of the observation signal vector, then the size of the filtering matrix is M × L. Also, this approach is more general and all the results from Chap. 2 are particular cases of the results derived in this chapter by just setting M = 1. The signal model is the same as in Sect. 2.1; so we start by explaining the principle of linear filtering with a rectangular matrix.
Jan Wouters - One of the best experts on this subject based on the ideXlab platform.
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the potential for speech intelligibility improvement using the ideal binary mask and the ideal wiener filter in single Channel Noise reduction systems application to auditory prostheses
IEEE Transactions on Audio Speech and Language Processing, 2013Co-Authors: Nilesh Madhu, Ann Spriet, Sofie Jansen, Raphael Koning, Jan WoutersAbstract:Whereas state-of-the-art single-Channel Noise reduction algorithms for auditory prostheses demonstrate an appreciable suppression of the Noise and improved speech quality, they are unable, thus far, to improve the intelligibility of Noise-degraded speech signals. Alternative approaches to speech enhancement using a binary time-frequency mask have demonstrated substantial intelligibility improvements in low signal-to-Noise-ratio (SNR) conditions under ideal settings, making this a promising research direction for auditory prostheses. These approaches exploit the sparsity and disjoint-ness of speech spectra in their short-time-frequency representation to preserve only the target-dominant time-frequency regions in the processed output. State-of-the-art Noise reduction algorithms in contrast are soft-decision approaches which weight each time-frequency region in proportion to the prevailing SNR. However, the potential for intelligibility improvement using these approaches has not been examined systematically vis-a-vis the binary mask alternative. This contribution compares the performance of an ideal soft-decision system, exemplified by the ideal Wiener filter (IWF), and the ideal binary mask (IBM) for single-Channel speech enhancement for auditory prostheses. To obtain results relevant to this application area, a (relatively) low spectral resolution, modelled using the Bark-spectrum scale, is used for both the IWF and the IBM. This spectral resolution is comparable to that being used in commercial hearing instruments. The comparison is in terms of potential for intelligibility improvement and resulting signal quality. Intelligibility tests carried out under various Noise conditions and SNRs show that the IWF leads to higher intelligibility scores than the IBM in low SNR conditions. Under non-ideal parameter estimates, it is demonstrated that the IWF approach is also much less sensitive to estimation errors. Quality-wise, a preference for the IWF exists. This was evaluated using a two-stage, pair-wise preference-rating test.
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preservation of interaural time delay for binaural hearing aids through multi Channel wiener filtering based Noise reduction
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Thomas Klasen, Marc Moonen, T Van Den Bogaert, Jan WoutersAbstract:The paper presents a binaural extension of a monaural multi-Channel Noise reduction algorithm for hearing aids based on Wiener filtering. The algorithm provides the hearing aid user with a binaural output. In addition to significantly suppressing the Noise interference, the algorithm preserves the interaural time delay (ITD) cues of the received speech, thus allowing the user to localize the speech source correctly.
K Zeger - One of the best experts on this subject based on the ideXlab platform.
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progressive image coding for noisy Channels
IEEE Signal Processing Letters, 1997Co-Authors: P G Sherwood, K ZegerAbstract:We cascade an existing image coder with carefully chosen error control coding, and thus produce a progressive image compression scheme whose performance on a noisy Channel is significantly better than that of previously known techniques. The main idea is to trade off the available transmission rate between source coding and Channel coding in an efficient manner. This coding system is easy to implement and has acceptably low complexity. Furthermore, effectively no degradation due to Channel Noise can be detected; instead, the penalty paid due to Channel Noise is a reduction in source coding resolution. Detailed numerical comparisons are given that can serve as benchmarks for comparisons with future encoding schemes. For example, for the 512/spl times/512 Lena image, at a transmission rate of 1 b/pixel, and for binary symmetric Channels with bit error probabilities 10/sup -3/, 10/sup -2/, and 10/sup -1/, the proposed system outperforms previously reported results by at least 2.6, 2.8, and 8.9 dB, respectively.
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progressive image coding on noisy Channels
Data Compression Conference, 1997Co-Authors: P G Sherwood, K ZegerAbstract:Numerous sophisticated techniques have been developed over the last several decades to efficiently transmit images across noisy Channels. Here, we cascade an existing image coder with carefully chosen error control coding, and thus produce a progressive image compression scheme whose performance on a noisy Channel is significantly better than that of previously known image compression techniques. The main idea is to trade off the available transmission rate between source coding and Channel coding in an efficient manner. This coding system is easy to implement and has acceptably low complexity. Furthermore, effectively no degradation due to Channel Noise can be detected; instead, the penalty paid due to Channel Noise is a reduction in source coding resolution. As an example, for the 512/spl times/512 Lena image, at an overall transmission rate of 1 bit per pixel, and for binary symmetric Channels with bit error probabilities 10/sup -3/, 10/sup -2/, and 10/sup -1/, the proposed system typically outperforms other existing systems by at least 2.6 dB, 2.8 dB, and 8.9 dB, respectively.
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empirical quantizer design in the presence of source Noise or Channel Noise
International Symposium on Information Theory, 1997Co-Authors: Tamas Linder, Gabor Lugosi, K ZegerAbstract:The problem of vector quantizer empirical design for noisy Channels or for noisy sources is studied. It is shown that the average squared distortion of a vector quantizer designed optimally from observing clean independent and identically distributed (i.i.d.) training vectors converges in expectation, as the training set size grows, to the minimum possible mean-squared error obtainable for quantizing the clean source and transmitting across a discrete memoryless noisy Channel. Similarly, it is shown that if the source is corrupted by additive Noise, then the average squared distortion of a vector quantizer designed optimally from observing i.i.d. noisy training vectors converges in expectation, as the training set size grows, to the minimum possible mean-squared error obtainable for quantizing the noisy source and transmitting across a Noiseless Channel. Rates of convergence are also provided.
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affine index assignments for binary lattice quantization with Channel Noise
International Symposium on Information Theory, 1995Co-Authors: A Mehes, K ZegerAbstract:Two major issues in noisy Channel vector quantization are complexity and sensitivity to Channel errors. Structured vector quantizers and index assignments provide a low complexity solution for enhancing Channel robustness. A general formula is given for the MSE performance of affine index assignments for a binary symmetric Channel with an arbitrary source and a binary lattice quantizer. The result is then used to compare some well-known redundancy free codes. The binary asymmetric Channel is considered for a uniform input distribution and a class of affine codes.
Nariman Farvardin - One of the best experts on this subject based on the ideXlab platform.
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subband image coding using entropy coded quantization over noisy Channels
IEEE Journal on Selected Areas in Communications, 1992Co-Authors: Nariman FarvardinAbstract:Under the assumption of Noiseless transmission the authors develop two entropy-coded subband image coding schemes. The difference between these schemes is the procedure used for encoding the lowest frequency subband: predictive coding is used in one system and transform coding in the other. After demonstrating the unacceptable sensitivity of these schemes to transmission Noise, the authors also develop a combined source/Channel coding scheme in which rate-compatible convolutional codes are used to provide protection against Channel Noise. A packetization scheme to prevent infinite error propagation is used and an algorithm for optimal assignment of bits between the source and Channel encoders of different subbands is developed. It is shown that, in the presence of Channel Noise, these Channel-optimized schemes offer dramatic performance improvements. >