The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
Richard Frayne - One of the best experts on this subject based on the ideXlab platform.
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a general description of linear time frequency Transforms and formulation of a fast Invertible Transform that samples the continuous s Transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-Transform, provides simultaneous time and frequency information similar to the wavelet Transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general Transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this Transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier Transform. When configured appropriately, this new algorithm samples the continuous S-Transform spectrum efficiently and nonredundantly, allowing signals to be Transformed in milliseconds rather than days, as compared to the original S-Transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a general description of linear time frequency Transforms and formulation of a fast Invertible Transform that samples the continuous s Transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-Transform, provides simultaneous time and frequency information similar to the wavelet Transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general Transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this Transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier Transform. When configured appropriately, this new algorithm samples the continuous S-Transform spectrum efficiently and nonredundantly, allowing signals to be Transformed in milliseconds rather than days, as compared to the original S-Transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
Peter Balazs - One of the best experts on this subject based on the ideXlab platform.
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A quasi-orthogonal, Invertible, and perceptually relevant time-frequency Transform for audio coding
2015Co-Authors: Olivier Derrien, Thibaud Necciari, Peter BalazsAbstract:We describe ERB-MDCT, an Invertible real-valued time-frequency Transform based on MDCT, which is widely used in audio coding (e.g. MP3 and AAC). ERB-MDCT was designed similarly to ERBLet, a recent Invertible Transform with a resolution evolving across frequency to match the perceptual ERB frequency scale, while the frequency scale in most Invertible Transforms (e.g. MDCT) is uniform. ERB-MDCT has mostly the same frequency scale as ERBLet, but the main improvement is that atoms are quasi-orthogonal, i.e. its redundancy is close to 1. Furthermore, the energy is more sparse in the time-frequency plane. Thus, it is more suitable for audio coding than ERBLet.
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EUSIPCO - A quasi-orthogonal, Invertible, and perceptually relevant time-frequency Transform for audio coding
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Olivier Derrien, Thibaud Necciarf, Peter BalazsAbstract:We describe ERB-MDCT, an Invertible real-valued time-frequency Transform based on MDCT, which is widely used in audio coding (e.g. MP3 and AAC). ERB-MDCT was designed similarly to ERBLet, a recent Invertible Transform with a resolution evolving across frequency to match the perceptual ERB frequency scale, while the frequency scale in most Invertible Transforms (e.g. MDCT) is uniform. ERB-MDCT has mostly the same frequency scale as ERBLet, but the main improvement is that atoms are quasi-orthogonal, i.e. its redundancy is close to 1. Furthermore, the energy is more sparse in the time-frequency plane. Thus, it is more suitable for audio coding than ERBLet.
Robert A Brown - One of the best experts on this subject based on the ideXlab platform.
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a general description of linear time frequency Transforms and formulation of a fast Invertible Transform that samples the continuous s Transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-Transform, provides simultaneous time and frequency information similar to the wavelet Transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general Transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this Transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier Transform. When configured appropriately, this new algorithm samples the continuous S-Transform spectrum efficiently and nonredundantly, allowing signals to be Transformed in milliseconds rather than days, as compared to the original S-Transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a general description of linear time frequency Transforms and formulation of a fast Invertible Transform that samples the continuous s Transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-Transform, provides simultaneous time and frequency information similar to the wavelet Transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general Transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this Transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier Transform. When configured appropriately, this new algorithm samples the continuous S-Transform spectrum efficiently and nonredundantly, allowing signals to be Transformed in milliseconds rather than days, as compared to the original S-Transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
Olivier Derrien - One of the best experts on this subject based on the ideXlab platform.
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A quasi-orthogonal, Invertible, and perceptually relevant time-frequency Transform for audio coding
2015Co-Authors: Olivier Derrien, Thibaud Necciari, Peter BalazsAbstract:We describe ERB-MDCT, an Invertible real-valued time-frequency Transform based on MDCT, which is widely used in audio coding (e.g. MP3 and AAC). ERB-MDCT was designed similarly to ERBLet, a recent Invertible Transform with a resolution evolving across frequency to match the perceptual ERB frequency scale, while the frequency scale in most Invertible Transforms (e.g. MDCT) is uniform. ERB-MDCT has mostly the same frequency scale as ERBLet, but the main improvement is that atoms are quasi-orthogonal, i.e. its redundancy is close to 1. Furthermore, the energy is more sparse in the time-frequency plane. Thus, it is more suitable for audio coding than ERBLet.
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EUSIPCO - A quasi-orthogonal, Invertible, and perceptually relevant time-frequency Transform for audio coding
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Olivier Derrien, Thibaud Necciarf, Peter BalazsAbstract:We describe ERB-MDCT, an Invertible real-valued time-frequency Transform based on MDCT, which is widely used in audio coding (e.g. MP3 and AAC). ERB-MDCT was designed similarly to ERBLet, a recent Invertible Transform with a resolution evolving across frequency to match the perceptual ERB frequency scale, while the frequency scale in most Invertible Transforms (e.g. MDCT) is uniform. ERB-MDCT has mostly the same frequency scale as ERBLet, but the main improvement is that atoms are quasi-orthogonal, i.e. its redundancy is close to 1. Furthermore, the energy is more sparse in the time-frequency plane. Thus, it is more suitable for audio coding than ERBLet.
M L Lauzon - One of the best experts on this subject based on the ideXlab platform.
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a general description of linear time frequency Transforms and formulation of a fast Invertible Transform that samples the continuous s Transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-Transform, provides simultaneous time and frequency information similar to the wavelet Transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general Transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this Transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier Transform. When configured appropriately, this new algorithm samples the continuous S-Transform spectrum efficiently and nonredundantly, allowing signals to be Transformed in milliseconds rather than days, as compared to the original S-Transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a general description of linear time frequency Transforms and formulation of a fast Invertible Transform that samples the continuous s Transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-Transform, provides simultaneous time and frequency information similar to the wavelet Transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general Transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this Transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier Transform. When configured appropriately, this new algorithm samples the continuous S-Transform spectrum efficiently and nonredundantly, allowing signals to be Transformed in milliseconds rather than days, as compared to the original S-Transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
