Ephraim Gower - Academia.edu (original) (raw)
Papers by Ephraim Gower
Journal of Electrical & Electronic Systems, 2018
Electronics Letters, 2013
An acoustic echo cancellation (AEC) algorithm based on minimising the mutual information between ... more An acoustic echo cancellation (AEC) algorithm based on minimising the mutual information between the loudspeaker and system output signals over a sliding discrete Fourier transform (DFT) window, for single AEC parameter estimation, is introduced. Unlike the conventional least-mean-square (LMS) systems, the proposed algorithm requires no double-talk detection (DTD) and its AEC parameter can be continually updated. Although it has been shown that independent component analysis (ICA) allows continual adaptation of the AEC parameters under DTD, current ICA-based algorithms estimate a filter of the same length as that of the LMS techniques. The sliding DFT window is utilised to facilitate adaptation of only one AEC parameter for deflation of the far-end signal, thereby greatly reducing the computational load.
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
In cellular networks, cells are grouped more densely around highly populated areas to provide mor... more In cellular networks, cells are grouped more densely around highly populated areas to provide more capacity. Antennas are pointed in accordance with local terrain and clutter to reduce signal shadows and interference. Hardware parameters are easily set during installation but difficult to change thereafter. In a dynamic environment of population migration, there is need to continuously tune network parameters to adapt the network performance. Modern mobile equipment logs network usage patterns and statistics over time. This information can be used to tune soft parameters of the network. These parameters may include frequency channel assignment or reuse, and transmitter radiation power assignment to provide more capacity on demand. The paper proposes that by combining the frequency and power assignments, further optimisation in resource allocation can be achieved over a traditional frequency assignment. The solution considers the interference, traffic intensity and use of priority fl...
In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm... more In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm is introduced where a sliding discrete Fourier transform window is adopted such that there is only one AEC parameter to estimate (reduced computational load), as opposed to thousands of coefficients modeling the room response. Conventional adaptive filtering techniques such as the least mean square (LMS) algorithm often fail under double-talk condition (and excessive noise) due to a corrupted measure of the objective function (i.e. minimization of the error output). Recent study has shown that ICA allows continual adaptation of the AEC parameters, hence it is adopted here as the optimization method of our AEC parameter. Simulation results are used to illustrate the superiority of the proposed algorithm over the LMS methods.
World Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 2013
In this paper, the statistical properties of filtered or convolved signals are considered by deri... more In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products. Keywords—Circular Convolution, linear Convolution, mixture density function. NOTATION A signal is a group of observations, and these are represented in vector form. For example, xi(n) = [xi(1), xi(2), · · · , xi(Ki)] is a vector for the i signal, for i [1, N ], whose observations are xi(n), for n [1,Ki], where Ki is the length of the i signal. Given a vector xi(n), the variable Xi (capit...
In this paper, an informax based source deflation algorithm of the loudspeaker (far-end) signal f... more In this paper, an informax based source deflation algorithm of the loudspeaker (far-end) signal for acoustic echo cancellation is introduced. The aim is to continually model the loudspeaker-environment-enclosure filter even under double-talk and noisy conditions, something the current methods fail to do. The deflation filter is learned using the informax principle where a prior knowledge about the near-end signal's approximate probability density function is required for optimal filter convergence. Simulation results are used to illustrate the performance of the algorithm under double-talk conditions, as well as simulation comparisons to the normalized least-mean-square algorithm for echo cancellation under varying noise conditions with no double-talk
World Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 2011
An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse deco... more An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm’s robustness to outliers. Keywords—expectation-maximization, Pitman estimator, sparse decomposition
This paper introduces a new point estimation algorithm, with particular focus on coherent noise s... more This paper introduces a new point estimation algorithm, with particular focus on coherent noise suppression, given several measurements of the device under test where it is assumed that 1) the noise is first-order stationery and 2) the device under test is linear and time-invariant. The algorithm exploits the robustness of the Pitman estimator of the Cauchy location parameter through the initial scaling of the test signal by a centred Gaussian variable of predetermined variance. It is illustrated through mathematical derivations and simulation results that the proposed algorithm is more accurate and consistently robust to outliers for different tailed density functions than the conventional methods of sample mean (coherent averaging technique) and sample median search. Keywords—Central limit theorem, Fisher-Cramer Rao, gamma function, Pitman estimator.
A frequency grouping approach for multi-channel instantaneous blind source separation (I-BSS) of ... more A frequency grouping approach for multi-channel instantaneous blind source separation (I-BSS) of convolutive mixtures is proposed for a lower net residual inter-symbol interference (ISI) and inter-channel interference (ICI) than the conventional short-time Fourier transform (STFT) approach. Starting in the time domain, STFTs are taken with overlapping windows to convert the convolutive mixing problem into frequency domain instantaneous mixing. Mixture samples at the same frequency but from different STFT windows are grouped together forming unique frequency groups. The individual frequency group vectors are input to the I-BSS algorithm of choice, from which the output samples are dispersed back to their respective STFT windows. After applying the inverse STFT, the resulting time domain signals are used to construct the complete source estimates via the weighted overlap-add method (WOLA). The proposed algorithm is tested for source deconvolution given two mixtures, and simulated alon...
In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm... more In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm is introduced where a sliding discrete Fourier transform window is adopted such that there is only one AEC parameter to estimate (reduced computational load), as opposed to thousands of coefficients modeling the room response. Conventional adaptive filtering techniques such as the least mean square (LMS) algorithm often fail under double-talk condition (and excessive noise) due to a corrupted measure of the objective function (i.e. minimization of the error output). Recent study has shown that ICA allows continual adaptation of the AEC parameters, hence it is adopted here as the optimization method of our AEC parameter. Simulation results are used to illustrate the superiority of the proposed algorithm over the LMS methods.
Journal of The Audio Engineering Society, 2009
... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - No... more ... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - November 2011 6 comments. ... Authors: Gower, Ephraim; Hawksford, Malcolm Affiliation: University of Essex, Colchester, UK AES Convention:126 (May 2009) Paper Number:7804 Import ...
... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - No... more ... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - November 2011 6 comments. ... Authors: Gower, Ephraim; Hawksford, Malcolm Affiliation: University of Essex, Colchester, UK AES Convention:126 (May 2009) Paper Number:7804 Import ...
In this study, a generic analysis of sensor impulse response effects on linearly filtered channel... more In this study, a generic analysis of sensor impulse response effects on linearly filtered channel noise is presented to illustrate the resulting variations to the Cramèr–Rao lower bounds (CRLBs) of signal parameter estimators in signal processing and communication applications. The authors start by deriving the density function of a filtered signal, which is shown to be a mixture density, and hence the exact expressions for the mean and variance. Simulation results are used to confirm the derivations, which are then used to investigate the effects of impulse response length and variance, as well as channel noise length and variance effects on the resulting CRLBs. Results indicate that for non-zero mean channel noise and impulse responses, the resulting mean of filtered noise can be relatively large causing adverse deviations to parameter estimations. The filtered noise variance is shown to be proportional to the impulse response energy, where for long duration of signal capture the ...
Journal of Electrical & Electronic Systems, 2018
Electronics Letters, 2013
An acoustic echo cancellation (AEC) algorithm based on minimising the mutual information between ... more An acoustic echo cancellation (AEC) algorithm based on minimising the mutual information between the loudspeaker and system output signals over a sliding discrete Fourier transform (DFT) window, for single AEC parameter estimation, is introduced. Unlike the conventional least-mean-square (LMS) systems, the proposed algorithm requires no double-talk detection (DTD) and its AEC parameter can be continually updated. Although it has been shown that independent component analysis (ICA) allows continual adaptation of the AEC parameters under DTD, current ICA-based algorithms estimate a filter of the same length as that of the LMS techniques. The sliding DFT window is utilised to facilitate adaptation of only one AEC parameter for deflation of the far-end signal, thereby greatly reducing the computational load.
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
In cellular networks, cells are grouped more densely around highly populated areas to provide mor... more In cellular networks, cells are grouped more densely around highly populated areas to provide more capacity. Antennas are pointed in accordance with local terrain and clutter to reduce signal shadows and interference. Hardware parameters are easily set during installation but difficult to change thereafter. In a dynamic environment of population migration, there is need to continuously tune network parameters to adapt the network performance. Modern mobile equipment logs network usage patterns and statistics over time. This information can be used to tune soft parameters of the network. These parameters may include frequency channel assignment or reuse, and transmitter radiation power assignment to provide more capacity on demand. The paper proposes that by combining the frequency and power assignments, further optimisation in resource allocation can be achieved over a traditional frequency assignment. The solution considers the interference, traffic intensity and use of priority fl...
In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm... more In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm is introduced where a sliding discrete Fourier transform window is adopted such that there is only one AEC parameter to estimate (reduced computational load), as opposed to thousands of coefficients modeling the room response. Conventional adaptive filtering techniques such as the least mean square (LMS) algorithm often fail under double-talk condition (and excessive noise) due to a corrupted measure of the objective function (i.e. minimization of the error output). Recent study has shown that ICA allows continual adaptation of the AEC parameters, hence it is adopted here as the optimization method of our AEC parameter. Simulation results are used to illustrate the superiority of the proposed algorithm over the LMS methods.
World Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 2013
In this paper, the statistical properties of filtered or convolved signals are considered by deri... more In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products. Keywords—Circular Convolution, linear Convolution, mixture density function. NOTATION A signal is a group of observations, and these are represented in vector form. For example, xi(n) = [xi(1), xi(2), · · · , xi(Ki)] is a vector for the i signal, for i [1, N ], whose observations are xi(n), for n [1,Ki], where Ki is the length of the i signal. Given a vector xi(n), the variable Xi (capit...
In this paper, an informax based source deflation algorithm of the loudspeaker (far-end) signal f... more In this paper, an informax based source deflation algorithm of the loudspeaker (far-end) signal for acoustic echo cancellation is introduced. The aim is to continually model the loudspeaker-environment-enclosure filter even under double-talk and noisy conditions, something the current methods fail to do. The deflation filter is learned using the informax principle where a prior knowledge about the near-end signal's approximate probability density function is required for optimal filter convergence. Simulation results are used to illustrate the performance of the algorithm under double-talk conditions, as well as simulation comparisons to the normalized least-mean-square algorithm for echo cancellation under varying noise conditions with no double-talk
World Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 2011
An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse deco... more An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm’s robustness to outliers. Keywords—expectation-maximization, Pitman estimator, sparse decomposition
This paper introduces a new point estimation algorithm, with particular focus on coherent noise s... more This paper introduces a new point estimation algorithm, with particular focus on coherent noise suppression, given several measurements of the device under test where it is assumed that 1) the noise is first-order stationery and 2) the device under test is linear and time-invariant. The algorithm exploits the robustness of the Pitman estimator of the Cauchy location parameter through the initial scaling of the test signal by a centred Gaussian variable of predetermined variance. It is illustrated through mathematical derivations and simulation results that the proposed algorithm is more accurate and consistently robust to outliers for different tailed density functions than the conventional methods of sample mean (coherent averaging technique) and sample median search. Keywords—Central limit theorem, Fisher-Cramer Rao, gamma function, Pitman estimator.
A frequency grouping approach for multi-channel instantaneous blind source separation (I-BSS) of ... more A frequency grouping approach for multi-channel instantaneous blind source separation (I-BSS) of convolutive mixtures is proposed for a lower net residual inter-symbol interference (ISI) and inter-channel interference (ICI) than the conventional short-time Fourier transform (STFT) approach. Starting in the time domain, STFTs are taken with overlapping windows to convert the convolutive mixing problem into frequency domain instantaneous mixing. Mixture samples at the same frequency but from different STFT windows are grouped together forming unique frequency groups. The individual frequency group vectors are input to the I-BSS algorithm of choice, from which the output samples are dispersed back to their respective STFT windows. After applying the inverse STFT, the resulting time domain signals are used to construct the complete source estimates via the weighted overlap-add method (WOLA). The proposed algorithm is tested for source deconvolution given two mixtures, and simulated alon...
In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm... more In this paper, an independent component analysis (ICA) acoustic echo cancellation (AEC) algorithm is introduced where a sliding discrete Fourier transform window is adopted such that there is only one AEC parameter to estimate (reduced computational load), as opposed to thousands of coefficients modeling the room response. Conventional adaptive filtering techniques such as the least mean square (LMS) algorithm often fail under double-talk condition (and excessive noise) due to a corrupted measure of the objective function (i.e. minimization of the error output). Recent study has shown that ICA allows continual adaptation of the AEC parameters, hence it is adopted here as the optimization method of our AEC parameter. Simulation results are used to illustrate the superiority of the proposed algorithm over the LMS methods.
Journal of The Audio Engineering Society, 2009
... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - No... more ... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - November 2011 6 comments. ... Authors: Gower, Ephraim; Hawksford, Malcolm Affiliation: University of Essex, Colchester, UK AES Convention:126 (May 2009) Paper Number:7804 Import ...
... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - No... more ... Perceptually-Motivated Objective Grading of Nonlinear Processing in Virtual-Bass Systems - November 2011 6 comments. ... Authors: Gower, Ephraim; Hawksford, Malcolm Affiliation: University of Essex, Colchester, UK AES Convention:126 (May 2009) Paper Number:7804 Import ...
In this study, a generic analysis of sensor impulse response effects on linearly filtered channel... more In this study, a generic analysis of sensor impulse response effects on linearly filtered channel noise is presented to illustrate the resulting variations to the Cramèr–Rao lower bounds (CRLBs) of signal parameter estimators in signal processing and communication applications. The authors start by deriving the density function of a filtered signal, which is shown to be a mixture density, and hence the exact expressions for the mean and variance. Simulation results are used to confirm the derivations, which are then used to investigate the effects of impulse response length and variance, as well as channel noise length and variance effects on the resulting CRLBs. Results indicate that for non-zero mean channel noise and impulse responses, the resulting mean of filtered noise can be relatively large causing adverse deviations to parameter estimations. The filtered noise variance is shown to be proportional to the impulse response energy, where for long duration of signal capture the ...