The Experts below are selected from a list of 45606 Experts worldwide ranked by ideXlab platform
Masato Akagi - One of the best experts on this subject based on the ideXlab platform.
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A method of Signal extraction from Noisy Signal based on auditory scene analysis
Speech Communication, 1999Co-Authors: Masashi Unoki, Masato AkagiAbstract:Abstract This paper proposes a method of extracting the desired Signal from a Noisy Signal, addressing the problem of segregating two acoustic sources as a model of acoustic source segregation based on Auditory Scene Analysis. Since the problem of segregating two acoustic sources is an ill-posed inverse problem, constraints are needed to determine a unique solution. The proposed method uses the four heuristic regularities proposed by Bregman as constraints and uses the instantaneous amplitude and phase of Noisy Signal components that have passed through a wavelet filterbank as features of acoustic sources. Then the model can extract the instantaneous amplitude and phase of the desired Signal. Simulations were performed to segregate the harmonic complex tone from a noise-added harmonic complex tone and to compare the results of using all or only some constraints. The results show that the method can segregate the harmonic complex tone precisely using all the constraints related to the four regularities and that the absence of some constraints reduces the accuracy.
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A method of Signal extraction from Noisy Signal based on auditory scene analysis
Speech Communication, 1999Co-Authors: Masashi Unoki, Masato AkagiAbstract:This paper proposes a method of extracting the desired Signal from a Noisy Signal, addressing the problem of segregating two acoustic sources as a model of acoustic source segregation based on Auditory Scene Analysis. Since the problem of segregating two acoustic sources is an ill-inverse problem, constraints are needed to determine a unique solution. The proposed method uses the four heuristic regularities proposed by Bregman as constraints and uses the instantaneous amplitude and phase of Noisy Signal components that have passed through a wavelet filterbank as features of acoustic sources. Then the model can extract the instantaneous amplitude and phase of the desired Signal. Simulations were performed to segregate the harmonic complex tone from a noise-added harmonic complex tone and to compare the results of using all or only some constraints. The results show that the method can segregate the harmonic complex tone precisely using all the constraints related to the four regularities and that the absence some constraints reduces the accuracy
Masashi Unoki - One of the best experts on this subject based on the ideXlab platform.
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A method of Signal extraction from Noisy Signal based on auditory scene analysis
Speech Communication, 1999Co-Authors: Masashi Unoki, Masato AkagiAbstract:Abstract This paper proposes a method of extracting the desired Signal from a Noisy Signal, addressing the problem of segregating two acoustic sources as a model of acoustic source segregation based on Auditory Scene Analysis. Since the problem of segregating two acoustic sources is an ill-posed inverse problem, constraints are needed to determine a unique solution. The proposed method uses the four heuristic regularities proposed by Bregman as constraints and uses the instantaneous amplitude and phase of Noisy Signal components that have passed through a wavelet filterbank as features of acoustic sources. Then the model can extract the instantaneous amplitude and phase of the desired Signal. Simulations were performed to segregate the harmonic complex tone from a noise-added harmonic complex tone and to compare the results of using all or only some constraints. The results show that the method can segregate the harmonic complex tone precisely using all the constraints related to the four regularities and that the absence of some constraints reduces the accuracy.
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A method of Signal extraction from Noisy Signal based on auditory scene analysis
Speech Communication, 1999Co-Authors: Masashi Unoki, Masato AkagiAbstract:This paper proposes a method of extracting the desired Signal from a Noisy Signal, addressing the problem of segregating two acoustic sources as a model of acoustic source segregation based on Auditory Scene Analysis. Since the problem of segregating two acoustic sources is an ill-inverse problem, constraints are needed to determine a unique solution. The proposed method uses the four heuristic regularities proposed by Bregman as constraints and uses the instantaneous amplitude and phase of Noisy Signal components that have passed through a wavelet filterbank as features of acoustic sources. Then the model can extract the instantaneous amplitude and phase of the desired Signal. Simulations were performed to segregate the harmonic complex tone from a noise-added harmonic complex tone and to compare the results of using all or only some constraints. The results show that the method can segregate the harmonic complex tone precisely using all the constraints related to the four regularities and that the absence some constraints reduces the accuracy
H.l. Van Trees - One of the best experts on this subject based on the ideXlab platform.
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A Signal subspace approach for speech enhancement
IEEE Transactions on Speech and Audio Processing, 1995Co-Authors: Y. Ephraim, H.l. Van TreesAbstract:A comprehensive approach for nonparametric speech enhancement is developed. The underlying principle is to decompose the vector space of the Noisy Signal into a Signal-plus-noise subspace and a noise subspace. Enhancement is performed by removing the noise subspace and estimating the clean Signal from the remaining Signal subspace. The decomposition can theoretically be performed by applying the Karhunen-Loeve transform (KLT) to the Noisy Signal. Linear estimation of the clean Signal is performed using two perceptually meaningful estimation criteria. First, Signal distortion is minimized while the residual noise energy is maintained below some given threshold. This criterion results in a Wiener filter with adjustable input noise level. Second, Signal distortion is minimized for a fixed spectrum of the residual noise. This criterion enables masking of the residual noise by the speech Signal. It results in a filter whose structure is similar to that obtained in the first case, except that now the gain function which modifies the KLT coefficients is solely dependent on the desired spectrum of the residual noise. The popular spectral subtraction speech enhancement approach is shown to be a particular case of the proposed approach. It is proven to be a Signal subspace approach which is optimal in an asymptotic (large sample) linear minimum mean square error sense, assuming the Signal and noise are stationary. Our listening tests indicate that 14 out of 16 listeners strongly preferred the proposed approach over the spectral subtraction approach.
Y. Jay Guo - One of the best experts on this subject based on the ideXlab platform.
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WCNC - Performance bounds of compressed sensing recovery algorithms for sparse Noisy Signals
2013 IEEE Wireless Communications and Networking Conference (WCNC), 2013Co-Authors: Qimei Cui, Xiaofeng Tao, Xianjun Yang, Waheed Ur Rehman, Y. Jay GuoAbstract:Recently, the performance bounds of the compressed sensing (CS) recovery algorithms have been investigated in the Noisy setting. However, most of the papers only focus on the Noisy measurement model where the Signal is noiseless and the noise enters after the CS operation. The Noisy Signal model where both the Signal and the compressed measurements are contaminated by the different noises is not considered. This paper works on the Noisy Signal model and provides the performance bounds for the following popular recovery algorithms: thresholding and orthogonal matching pursuit (OMP), Dantzig selector (DS) and basis pursuit denoising (BPDN). The performance of the recovery algorithms is quantified as the l 2 distance between the reconstructed Signal and the true Noisy Signal. Next, the impacts of the noise are analyzed on the basis of the quantified performance. The analysis results show that the effective way to restrain the impact of the noise is to choose the measurement matrix with low correlation between the columns or the rows. Finally, the theoretical bounds are verified with numerical simulations by calculating the mean-squared-error for the different noise variances. The simulation results show that OMP owns the better performance than the other three recovery algorithms under the Noisy Signal model.
B Natarajan - One of the best experts on this subject based on the ideXlab platform.
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filtering random noise from deterministic Signals via data compression
IEEE Transactions on Signal Processing, 1995Co-Authors: B NatarajanAbstract:We present a novel technique for the design of filters for random noise, leading to a class of filters called Occam filters. The essence of the technique is that when a lossy data compression algorithm is applied to a Noisy Signal with the allowed loss set equal to the noise strength, the loss and the noise tend to cancel rather than add. We give two illustrative applications of the technique to univariate Signals. We also prove asymptotic convergence bounds on the effectiveness of Occam filters.
