The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
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On widely linear Wiener and tradeoff filters for Noise Reduction
Speech Communication, 2010Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng HuangAbstract:Noise Reduction is often formulated as a linear filtering problem in the frequency domain. With this formulation, the core issue of Noise Reduction becomes how to design an optimal frequency-domain filter that can significantly suppress Noise without introducing perceptually noticeable speech distortion. While higher-order information can be used, most existing approaches use only second-order statistics to design the Noise-Reduction filter because they are relatively easier to estimate and are more reliable. When we transform non-stationary speech signals into the frequency domain and work with the short-time discrete Fourier transform coefficients, there are two types of second-order statistics, i.e., the variance and the so-called pseudo-variance due to the noncircularity of the signal. So far, only the variance information has been exploited in designing different Noise-Reduction filters while the pseudo-variance has been neglected. In this paper, we attempt to shed some light on how to use noncircularity in the context of Noise Reduction. We will discuss the design of optimal and suboptimal Noise Reduction filters using both the variance and pseudo-variance and answer the basic question whether noncircularity can be used to improve the Noise-Reduction performance.
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Noise Reduction in Speech Processing
2009Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Israel CohenAbstract:Noise is everywhere and in most applications that are related to audio and speech, such as human-machine interfaces, hands-free communications, voice over IP (VoIP), hearing aids, teleconferencing/telepresence/telecollaboration systems, and so many others, the signal of interest (usually speech) that is picked up by a microphone is generally contaminated by Noise. As a result, the microphone signal has to be cleaned up with digital signal processing tools before it is stored, analyzed, transmitted, or played out. This cleaning process is often called Noise Reduction and this topic has attracted a considerable amount of research and engineering attention for several decades. One of the objectives of this book is to present in a common framework an overview of the state of the art of Noise Reduction algorithms in the single-channel (one microphone) case. The focus is on the most useful approaches, i.e., filtering techniques (in different domains) and spectral enhancement methods. The other objective of Noise Reduction in Speech Processing is to derive all these well-known techniques in a rigorous way and prove many fundamental and intuitive results often taken for granted. This book is especially written for graduate students and research engineers who work on Noise Reduction for speech and audio applications and want to understand the subtle mechanisms behind each approach. Many new and interesting concepts are presented in this text that we hope the readers will find useful and inspiring.
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Noise Reduction Algorithms in a Generalized Transform Domain
IEEE Transactions on Audio Speech and Language Processing, 2009Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Arden HuangAbstract:Noise Reduction for speech applications is often formulated as a digital filtering problem, where the clean speech estimate is obtained by passing the noisy speech through a linear filter/transform. With such a formulation, the core issue of Noise Reduction becomes how to design an optimal filter (based on the statistics of the speech and Noise signals) that can significantly suppress Noise without introducing perceptually noticeable speech distortion. The optimal filters can be designed either in the time or in a transform domain. The advantage of working in a transform space is that, if the transform is selected properly, the speech and Noise signals may be better separated in that space, thereby enabling better filter estimation and Noise Reduction performance. Although many different transforms exist, most efforts in the field of Noise Reduction have been focused only on the Fourier and Karhunen-Loeve transforms. Even with these two, no formal study has been carried out to investigate which transform can outperform the other. In this paper, we reformulate the Noise Reduction problem into a more generalized transform domain. We will show some of the advantages of working in this generalized domain, such as 1) different transforms can be used to replace each other without any requirement to change the algorithm (optimal filter) formulation, and 2) it is easier to fairly compare different transforms for their Noise Reduction performance. We will also address how to design different optimal and suboptimal filters in such a generalized transform domain.
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New insights into the Noise Reduction Wiener filter
IEEE Transactions on Audio Speech and Language Processing, 2006Co-Authors: Jingdong Chen, Yiteng Huang, Jacob Benesty, Simon DocloAbstract: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
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study of the wiener filter for Noise Reduction
2005Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Simon DocloAbstract: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.
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On widely linear Wiener and tradeoff filters for Noise Reduction
Speech Communication, 2010Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng HuangAbstract:Noise Reduction is often formulated as a linear filtering problem in the frequency domain. With this formulation, the core issue of Noise Reduction becomes how to design an optimal frequency-domain filter that can significantly suppress Noise without introducing perceptually noticeable speech distortion. While higher-order information can be used, most existing approaches use only second-order statistics to design the Noise-Reduction filter because they are relatively easier to estimate and are more reliable. When we transform non-stationary speech signals into the frequency domain and work with the short-time discrete Fourier transform coefficients, there are two types of second-order statistics, i.e., the variance and the so-called pseudo-variance due to the noncircularity of the signal. So far, only the variance information has been exploited in designing different Noise-Reduction filters while the pseudo-variance has been neglected. In this paper, we attempt to shed some light on how to use noncircularity in the context of Noise Reduction. We will discuss the design of optimal and suboptimal Noise Reduction filters using both the variance and pseudo-variance and answer the basic question whether noncircularity can be used to improve the Noise-Reduction performance.
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Noise Reduction in Speech Processing
2009Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Israel CohenAbstract:Noise is everywhere and in most applications that are related to audio and speech, such as human-machine interfaces, hands-free communications, voice over IP (VoIP), hearing aids, teleconferencing/telepresence/telecollaboration systems, and so many others, the signal of interest (usually speech) that is picked up by a microphone is generally contaminated by Noise. As a result, the microphone signal has to be cleaned up with digital signal processing tools before it is stored, analyzed, transmitted, or played out. This cleaning process is often called Noise Reduction and this topic has attracted a considerable amount of research and engineering attention for several decades. One of the objectives of this book is to present in a common framework an overview of the state of the art of Noise Reduction algorithms in the single-channel (one microphone) case. The focus is on the most useful approaches, i.e., filtering techniques (in different domains) and spectral enhancement methods. The other objective of Noise Reduction in Speech Processing is to derive all these well-known techniques in a rigorous way and prove many fundamental and intuitive results often taken for granted. This book is especially written for graduate students and research engineers who work on Noise Reduction for speech and audio applications and want to understand the subtle mechanisms behind each approach. Many new and interesting concepts are presented in this text that we hope the readers will find useful and inspiring.
-
Noise Reduction Algorithms in a Generalized Transform Domain
IEEE Transactions on Audio Speech and Language Processing, 2009Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Arden HuangAbstract:Noise Reduction for speech applications is often formulated as a digital filtering problem, where the clean speech estimate is obtained by passing the noisy speech through a linear filter/transform. With such a formulation, the core issue of Noise Reduction becomes how to design an optimal filter (based on the statistics of the speech and Noise signals) that can significantly suppress Noise without introducing perceptually noticeable speech distortion. The optimal filters can be designed either in the time or in a transform domain. The advantage of working in a transform space is that, if the transform is selected properly, the speech and Noise signals may be better separated in that space, thereby enabling better filter estimation and Noise Reduction performance. Although many different transforms exist, most efforts in the field of Noise Reduction have been focused only on the Fourier and Karhunen-Loeve transforms. Even with these two, no formal study has been carried out to investigate which transform can outperform the other. In this paper, we reformulate the Noise Reduction problem into a more generalized transform domain. We will show some of the advantages of working in this generalized domain, such as 1) different transforms can be used to replace each other without any requirement to change the algorithm (optimal filter) formulation, and 2) it is easier to fairly compare different transforms for their Noise Reduction performance. We will also address how to design different optimal and suboptimal filters in such a generalized transform domain.
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New insights into the Noise Reduction Wiener filter
IEEE Transactions on Audio Speech and Language Processing, 2006Co-Authors: Jingdong Chen, Yiteng Huang, Jacob Benesty, Simon DocloAbstract: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
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study of the wiener filter for Noise Reduction
2005Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Simon DocloAbstract: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.
Yiteng Huang - One of the best experts on this subject based on the ideXlab platform.
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On widely linear Wiener and tradeoff filters for Noise Reduction
Speech Communication, 2010Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng HuangAbstract:Noise Reduction is often formulated as a linear filtering problem in the frequency domain. With this formulation, the core issue of Noise Reduction becomes how to design an optimal frequency-domain filter that can significantly suppress Noise without introducing perceptually noticeable speech distortion. While higher-order information can be used, most existing approaches use only second-order statistics to design the Noise-Reduction filter because they are relatively easier to estimate and are more reliable. When we transform non-stationary speech signals into the frequency domain and work with the short-time discrete Fourier transform coefficients, there are two types of second-order statistics, i.e., the variance and the so-called pseudo-variance due to the noncircularity of the signal. So far, only the variance information has been exploited in designing different Noise-Reduction filters while the pseudo-variance has been neglected. In this paper, we attempt to shed some light on how to use noncircularity in the context of Noise Reduction. We will discuss the design of optimal and suboptimal Noise Reduction filters using both the variance and pseudo-variance and answer the basic question whether noncircularity can be used to improve the Noise-Reduction performance.
-
Noise Reduction in Speech Processing
2009Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Israel CohenAbstract:Noise is everywhere and in most applications that are related to audio and speech, such as human-machine interfaces, hands-free communications, voice over IP (VoIP), hearing aids, teleconferencing/telepresence/telecollaboration systems, and so many others, the signal of interest (usually speech) that is picked up by a microphone is generally contaminated by Noise. As a result, the microphone signal has to be cleaned up with digital signal processing tools before it is stored, analyzed, transmitted, or played out. This cleaning process is often called Noise Reduction and this topic has attracted a considerable amount of research and engineering attention for several decades. One of the objectives of this book is to present in a common framework an overview of the state of the art of Noise Reduction algorithms in the single-channel (one microphone) case. The focus is on the most useful approaches, i.e., filtering techniques (in different domains) and spectral enhancement methods. The other objective of Noise Reduction in Speech Processing is to derive all these well-known techniques in a rigorous way and prove many fundamental and intuitive results often taken for granted. This book is especially written for graduate students and research engineers who work on Noise Reduction for speech and audio applications and want to understand the subtle mechanisms behind each approach. Many new and interesting concepts are presented in this text that we hope the readers will find useful and inspiring.
-
New insights into the Noise Reduction Wiener filter
IEEE Transactions on Audio Speech and Language Processing, 2006Co-Authors: Jingdong Chen, Yiteng Huang, Jacob Benesty, Simon DocloAbstract: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
2005Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Simon DocloAbstract: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.
Simon Doclo - One of the best experts on this subject based on the ideXlab platform.
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New insights into the Noise Reduction Wiener filter
IEEE Transactions on Audio Speech and Language Processing, 2006Co-Authors: Jingdong Chen, Yiteng Huang, Jacob Benesty, Simon DocloAbstract: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
2005Co-Authors: Jacob Benesty, Yiteng Huang, Jingdong Chen, Simon DocloAbstract: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.
David Chelidze - One of the best experts on this subject based on the ideXlab platform.
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smooth local subspace projection for nonlinear Noise Reduction
Chaos, 2014Co-Authors: David ChelidzeAbstract:Many nonlinear or chaotic time series exhibit an innate broad spectrum, which makes Noise Reduction difficult. Local projective Noise Reduction is one of the most effective tools. It is based on proper orthogonal decomposition (POD) and works for both map-like and continuously sampled time series. However, POD only looks at geometrical or topological properties of data and does not take into account the temporal characteristics of time series. Here, we present a new smooth projective Noise Reduction method. It uses smooth orthogonal decomposition (SOD) of bundles of reconstructed short-time trajectory strands to identify smooth local subspaces. Restricting trajectories to these subspaces imposes temporal smoothness on the filtered time series. It is shown that SOD-based Noise Reduction significantly outperforms the POD-based method for continuously sampled noisy time series.
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Smooth Projective Nonlinear Noise Reduction
Volume 8: 22nd Reliability Stress Analysis and Failure Prevention Conference; 25th Conference on Mechanical Vibration and Noise, 2013Co-Authors: David ChelidzeAbstract:Many nonlinear or chaotic time series exhibit an innate broad spectrum, which makes Noise Reduction difficult. Locally projective Noise Reduction is one of the most effective tools. It is based on proper orthogonal decomposition (POD), and works for both map-like and continuously sampled time series. However, POD only looks at geometrical or topological properties of data and does not take into account the temporal characteristics of time series. Here we present a new smooth projective Noise Reduction method. It uses bundles of locally reconstructed trajectory strands and their smooth orthogonal decomposition (SOD) to identify smooth local subspaces. Restricting trajectories to these subspaces imposes temporal smoothness on the filtered time series. It is shown that SOD-based Noise Reduction significantly outperforms the POD-based method for continuously sampled noisy time series.Copyright © 2013 by ASME
