The Experts below are selected from a list of 29220 Experts worldwide ranked by ideXlab platform
Rainer Martin - One of the best experts on this subject based on the ideXlab platform.
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INTERSPEECH - Phase estimation for Signal Reconstruction in single-channel source separation
2020Co-Authors: Rahim Saeidi, Pejman Mowlaee, Rainer MartinAbstract:Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced Signal. Instead, they directly employ the mixed-Signal phase for Signal Reconstruction which leads to undesired traces of the interfering source in the target Signal. In this paper, assuming a given knowledge of Signal spectrum amplitude, we present a solution to estimate the phase information for Signal Reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal Signal spectra assumption by perturbing the clean Signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude Signal spectra, the proposed phase estimation approach offers improved Signal Reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.
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phase estimation for Signal Reconstruction in single channel source separation
Conference of the International Speech Communication Association, 2012Co-Authors: Rahim Saeidi, Pejman Mowlaee, Rainer MartinAbstract:Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced Signal. Instead, they directly employ the mixed-Signal phase for Signal Reconstruction which leads to undesired traces of the interfering source in the target Signal. In this paper, assuming a given knowledge of Signal spectrum amplitude, we present a solution to estimate the phase information for Signal Reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal Signal spectra assumption by perturbing the clean Signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude Signal spectra, the proposed phase estimation approach offers improved Signal Reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.
Rahim Saeidi - One of the best experts on this subject based on the ideXlab platform.
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INTERSPEECH - Phase estimation for Signal Reconstruction in single-channel source separation
2020Co-Authors: Rahim Saeidi, Pejman Mowlaee, Rainer MartinAbstract:Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced Signal. Instead, they directly employ the mixed-Signal phase for Signal Reconstruction which leads to undesired traces of the interfering source in the target Signal. In this paper, assuming a given knowledge of Signal spectrum amplitude, we present a solution to estimate the phase information for Signal Reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal Signal spectra assumption by perturbing the clean Signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude Signal spectra, the proposed phase estimation approach offers improved Signal Reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.
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phase estimation for Signal Reconstruction in single channel source separation
Conference of the International Speech Communication Association, 2012Co-Authors: Rahim Saeidi, Pejman Mowlaee, Rainer MartinAbstract:Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced Signal. Instead, they directly employ the mixed-Signal phase for Signal Reconstruction which leads to undesired traces of the interfering source in the target Signal. In this paper, assuming a given knowledge of Signal spectrum amplitude, we present a solution to estimate the phase information for Signal Reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal Signal spectra assumption by perturbing the clean Signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude Signal spectra, the proposed phase estimation approach offers improved Signal Reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.
Pejman Mowlaee - One of the best experts on this subject based on the ideXlab platform.
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INTERSPEECH - Phase estimation for Signal Reconstruction in single-channel source separation
2020Co-Authors: Rahim Saeidi, Pejman Mowlaee, Rainer MartinAbstract:Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced Signal. Instead, they directly employ the mixed-Signal phase for Signal Reconstruction which leads to undesired traces of the interfering source in the target Signal. In this paper, assuming a given knowledge of Signal spectrum amplitude, we present a solution to estimate the phase information for Signal Reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal Signal spectra assumption by perturbing the clean Signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude Signal spectra, the proposed phase estimation approach offers improved Signal Reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.
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ICASSP - On the importance of harmonic phase modification for improved speech Signal Reconstruction
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Anna Maly, Pejman MowlaeeAbstract:Conventional single-channel speech enhancement is mainly focused on modifying the noisy short-time Fourier transform amplitude spectrum while for Signal Reconstruction the noisy phase is used. Recent advances demonstrate the positive improvements in speech enhancement when the noisy phase is replaced with an estimated clean phase for Signal Reconstruction. In this paper, we study the impact of the linear phase and unwrapped phase components provided by harmonic phase decomposition on the speech quality at Signal Reconstruction. We present objective and subjective results comparing the proposed harmonic phase modification with other phase estimation methods. Our results show that enhancement of decomposed phase parts suffices for improved Reconstruction in speech enhancement
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phase estimation for Signal Reconstruction in single channel source separation
Conference of the International Speech Communication Association, 2012Co-Authors: Rahim Saeidi, Pejman Mowlaee, Rainer MartinAbstract:Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced Signal. Instead, they directly employ the mixed-Signal phase for Signal Reconstruction which leads to undesired traces of the interfering source in the target Signal. In this paper, assuming a given knowledge of Signal spectrum amplitude, we present a solution to estimate the phase information for Signal Reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal Signal spectra assumption by perturbing the clean Signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude Signal spectra, the proposed phase estimation approach offers improved Signal Reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.
Pramod P. Khargonekar - One of the best experts on this subject based on the ideXlab platform.
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Signal Reconstruction via h infty sampled data control theory beyond the shannon paradigm
IEEE Transactions on Signal Processing, 2012Co-Authors: Yutaka Yamamoto, Masaaki Nagahara, Pramod P. KhargonekarAbstract:This paper presents a new method for Signal Reconstruction by leveraging sampled-data control theory. We formulate the Signal Reconstruction problem in terms of an analog performance optimization problem using a stable discrete-time filter. The proposed H∞ performance criterion naturally takes inter-sample behavior into account, reflecting the energy distributions of the Signal. We present methods for computing optimal solutions which are guaranteed to be stable and causal. Detailed comparisons to alternative methods are provided. We discuss some applications in sound and image Reconstruction.
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Signal Reconstruction via $H^{\infty}$ Sampled-Data Control Theory—Beyond the Shannon Paradigm
IEEE Transactions on Signal Processing, 2012Co-Authors: Yutaka Yamamoto, Masaaki Nagahara, Pramod P. KhargonekarAbstract:This paper presents a new method for Signal Reconstruction by leveraging sampled-data control theory. We formulate the Signal Reconstruction problem in terms of an analog performance optimization problem using a stable discrete-time filter. The proposed H∞ performance criterion naturally takes inter-sample behavior into account, reflecting the energy distributions of the Signal. We present methods for computing optimal solutions which are guaranteed to be stable and causal. Detailed comparisons to alternative methods are provided. We discuss some applications in sound and image Reconstruction.
Yutaka Yamamoto - One of the best experts on this subject based on the ideXlab platform.
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Signal Reconstruction via h infty sampled data control theory beyond the shannon paradigm
IEEE Transactions on Signal Processing, 2012Co-Authors: Yutaka Yamamoto, Masaaki Nagahara, Pramod P. KhargonekarAbstract:This paper presents a new method for Signal Reconstruction by leveraging sampled-data control theory. We formulate the Signal Reconstruction problem in terms of an analog performance optimization problem using a stable discrete-time filter. The proposed H∞ performance criterion naturally takes inter-sample behavior into account, reflecting the energy distributions of the Signal. We present methods for computing optimal solutions which are guaranteed to be stable and causal. Detailed comparisons to alternative methods are provided. We discuss some applications in sound and image Reconstruction.
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Signal Reconstruction via $H^{\infty}$ Sampled-Data Control Theory—Beyond the Shannon Paradigm
IEEE Transactions on Signal Processing, 2012Co-Authors: Yutaka Yamamoto, Masaaki Nagahara, Pramod P. KhargonekarAbstract:This paper presents a new method for Signal Reconstruction by leveraging sampled-data control theory. We formulate the Signal Reconstruction problem in terms of an analog performance optimization problem using a stable discrete-time filter. The proposed H∞ performance criterion naturally takes inter-sample behavior into account, reflecting the energy distributions of the Signal. We present methods for computing optimal solutions which are guaranteed to be stable and causal. Detailed comparisons to alternative methods are provided. We discuss some applications in sound and image Reconstruction.
