摘要 :
Signal undersampling is a technique used in digital signal processing where the sampling rate is less than what the sampling theorem requires. In this case, there is the effect of aliasing, which can lead to unacceptable distortio...
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Signal undersampling is a technique used in digital signal processing where the sampling rate is less than what the sampling theorem requires. In this case, there is the effect of aliasing, which can lead to unacceptable distortion. The paper proposes a method to restore the spectrum distorted by aliasing. To do this, the signal is sampled in several channels with different sampling rates. All these frequencies do not satisfy the sampling theorem, so aliasing takes place in all channels. However, the spectrum distortions in the channels differ from each other. These differences are used to restore the spectrum. The paper presents an example of spectrum restoration in the case of three channels.
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摘要 :
Signal undersampling is a technique used in digital signal processing where the sampling rate is less than what the sampling theorem requires. In this case, there is the effect of aliasing, which can lead to unacceptable distortio...
展开
Signal undersampling is a technique used in digital signal processing where the sampling rate is less than what the sampling theorem requires. In this case, there is the effect of aliasing, which can lead to unacceptable distortion. The paper proposes a method to restore the spectrum distorted by aliasing. To do this, the signal is sampled in several channels with different sampling rates. All these frequencies do not satisfy the sampling theorem, so aliasing takes place in all channels. However, the spectrum distortions in the channels differ from each other. These differences are used to restore the spectrum. The paper presents an example of spectrum restoration in the case of three channels.
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摘要 :
This work deals with denosing of a one-dimensional signal corrupted by additive white Gaussian noise. A common way to solve the problem is to utilize the total variation (TV) method. Basically, the TV regularization minimizes a fu...
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This work deals with denosing of a one-dimensional signal corrupted by additive white Gaussian noise. A common way to solve the problem is to utilize the total variation (TV) method. Basically, the TV regularization minimizes a functional consisting of the sum of fidelity and regularization terms. We derive explicit solutions of the one-dimensional TV regularization problem that help us to restore noisy signals with a direct, non-iterative algorithm. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of noisy signals.
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摘要 :
This work deals with denosing of a one-dimensional signal corrupted by additive white Gaussian noise. A common way to solve the problem is to utilize the total variation (TV) method. Basically, the TV regularization minimizes a fu...
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This work deals with denosing of a one-dimensional signal corrupted by additive white Gaussian noise. A common way to solve the problem is to utilize the total variation (TV) method. Basically, the TV regularization minimizes a functional consisting of the sum of fidelity and regularization terms. We derive explicit solutions of the one-dimensional TV regularization problem that help us to restore noisy signals with a direct, non-iterative algorithm. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of noisy signals.
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摘要 :
This work deals with denosing of a one-dimensional signal corrupted by additive white Gaussian noise. A common way to solve the problem is to utilize the total variation (TV) method. Basically, the TV regularization minimizes a fu...
展开
This work deals with denosing of a one-dimensional signal corrupted by additive white Gaussian noise. A common way to solve the problem is to utilize the total variation (TV) method. Basically, the TV regularization minimizes a functional consisting of the sum of fidelity and regularization terms. We derive explicit solutions of the one-dimensional TV regularization problem that help us to restore noisy signals with a direct, non-iterative algorithm. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of noisy signals.
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摘要 :
One of the methods for input signal restoration during dynamic pressure measurement in real time and its relevant piezoresistive sensor are proposed.
摘要 :
One of the methods for input signal restoration during dynamic pressure measurement in real time and its relevant piezoresistive sensor are proposed.
摘要 :
We present a new statistical technique for the estimation of the high frequency components (4-8kHz) of speech signals from narrow-band (0-4 kHz) signals. The magnitude spectra of broadband speech are modelled as the outcome of a P...
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We present a new statistical technique for the estimation of the high frequency components (4-8kHz) of speech signals from narrow-band (0-4 kHz) signals. The magnitude spectra of broadband speech are modelled as the outcome of a Polya Urn process, that represents the spectra as the histogram of the outcome of several draws from a mixture multinomial distribution over frequency indices. The multinomial distributions that compose this process are learnt from a corpus of broadband (0-8kHz) speech. To estimate high-frequency components of narrow-band speech, its spectra are also modelled as the outcome of draws from a mixture-multinomial process that is composed of the learnt multinomials, where the counts of the indices of higher frequencies have been obscured. The obscured high-frequency components are then estimated as the expected number of draws of their indices from the mixture-multinomial. Experiments conducted on bandlimited signals derived from the WSJ corpus show that the proposed procedure is able to accurately estimate the high frequency components of these signals.
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摘要 :
We present a new statistical technique for the estimation of the high frequency components (4-8kHz) of speech signals from narrow-band (0-4kHz) signals. The magnitude spectra of broadband speech are modelled as the outcome of a Po...
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We present a new statistical technique for the estimation of the high frequency components (4-8kHz) of speech signals from narrow-band (0-4kHz) signals. The magnitude spectra of broadband speech are modelled as the outcome of a Polya Urn process, that represents the spectra as the histogram of the outcome of several draws from a mixture multinomial distribution over frequency indices. The multinomial distributions that compose this process are learnt from a corpus of broadband (0-8kHz) speech To estimate high-frequency components of narrow-band speech, its spectra are also modelled as the outcome of draws from a mixture-multinomial process that is composed of the learnt multinomials, where the counts of the indices of higher frequencies have been obscured. The obscured high-frequency components are then estimated as the expected number of draws of their indices from the mixture-multinomial. Experiments conducted on bandlimited signals derived from the WSJ corpus show that the proposed procedure is able to accurately estimate the high frequency components of these signals.
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