The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Youshen Xia - One of the best experts on this subject based on the ideXlab platform.
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Effective Kalman filtering algorithm for distributed Multichannel speech enhancement
Neurocomputing, 2018Co-Authors: Youshen XiaAbstract:Abstract Kalman filtering is known as an effective speech enhancement technique. Many Kalman filtering algorithms for single channel speech enhancement were developed in past decades. However, the Kalman filtering algorithm for Multichannel speech enhancement is very less. This paper proposes a Kalman filtering algorithm for distributed Multichannel speech enhancement in the time domain under colored noise environment. Compared with conventional algorithms for distributed Multichannel speech enhancement, the proposed algorithm has lower computational complexity and requires less computational resources. Simulation results show that the proposed algorithm is superior to the conventional algorithms for distributed Multichannel speech enhancement in achieving higher noise reduction, less signal distortion and more speech intelligibility. Moreover, the proposed algorithm has a faster speed than several Multichannel speech enhancement algorithms.
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fast distributed Multichannel speech enhancement using novel frequency domain estimators of magnitude squared spectrum
Speech Communication, 2015Co-Authors: Youshen XiaAbstract:Abstract This paper proposes two novel frequency domain estimators for fast distributed Multichannel speech enhancement in background of white and colored noise. The proposed two frequency domain estimators are maximum a posterior (MAP) and minimum mean square error (MMSE) estimators, respectively. They significantly generalize two single channel optimal frequency domain estimators of magnitude-squared spectrum. Compared with the optimal Multichannel frequency domain estimator generalizing the single channel short-time spectral amplitude and log-spectral amplitude estimators, the proposed two frequency domain estimators have a very low computational cost. Computed results show that the proposed two estimators reduce both colored background noise and speech distortion. Furthermore, the proposed two Multichannel algorithms have a much faster computational speed than conventional Multichannel algorithms for distributed Multichannel speech enhancement.
Patrick Perez - One of the best experts on this subject based on the ideXlab platform.
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Multichannel audio declipping
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2016Co-Authors: Alexey Ozerov, Cagdas Bilen, Patrick PerezAbstract:Audio declipping consists in recovering so-called clipped audio samples that are set to a maximum / minimum threshold. Many different approaches were proposed to solve this problem in case of singlechannel (mono) recordings. However, while most of audio recordings are Multichannel nowadays, there is no method designed specifically for Multichannel audio declipping, where the inter-channel correlations may be efficiently exploited for a better declipping result. In this work we propose for the first time such a Multichannel audio declipping method. Our method is based on representing a Multichannel audio recording as a convolutive mixture of several audio sources, and on modeling the source power spectrograms and mixing filters by nonnegative tensor factorization model and full-rank covariance matrices, respectively. A generalized expectation-maximization algorithm is proposed to estimate model parameters. It is shown experimentally that the proposed Multichannel audio de-clipping algorithm outperforms in average and in most cases a state-of-the-art single-channel declipping algorithm applied to each channel independently.
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ICASSP - Multichannel audio declipping
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Alexey Ozerov, Cagdas Bilen, Patrick PerezAbstract:Audio declipping consists in recovering so-called clipped audio samples that are set to a maximum / minimum threshold. Many different approaches were proposed to solve this problem in case of singlechannel (mono) recordings. However, while most of audio recordings are Multichannel nowadays, there is no method designed specifically for Multichannel audio declipping, where the inter-channel correlations may be efficiently exploited for a better declipping result. In this work we propose for the first time such a Multichannel audio declipping method. Our method is based on representing a Multichannel audio recording as a convolutive mixture of several audio sources, and on modeling the source power spectrograms and mixing filters by nonnegative tensor factorization model and full-rank covariance matrices, respectively. A generalized expectation-maximization algorithm is proposed to estimate model parameters. It is shown experimentally that the proposed Multichannel audio de-clipping algorithm outperforms in average and in most cases a state-of-the-art single-channel declipping algorithm applied to each channel independently.
Pascale Minet - One of the best experts on this subject based on the ideXlab platform.
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Multichannel assignment protocols in wireless sensor networks
Pervasive and Mobile Computing, 2015Co-Authors: Ridha Soua, Pascale MinetAbstract:With the spectacular development in radio and MEMS technologies, having sensor nodes capable of efficiently tuning their frequency over different channels is becoming more and more straightforward. For instance, TelosB motes can communicate on multiple frequencies as specified in the 802.15.4 standard. This reality has given birth to the Multichannel communication paradigm in Wireless Sensor Networks (WSNs). While, Multichannel communication obviously mitigates interferences, jamming and congestion, it also raises a number of challenging issues. In this paper, we set out to present a picture of Multichannel assignment protocols in WSNs. After identifying the reasons of resorting to Multichannel communication paradigm in WSNs and the specific issues that should be tackled, we propose a classification of Multichannel assignment protocols, pointing out different channel selection policies, channel assignment categories and channel assignment methods. We conclude by a recapitulative table presenting many examples of existing Multichannel protocols designed for WSNs and highlight promising research directions.
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A survey on Multichannel assignment protocols in Wireless Sensor Networks
2011Co-Authors: Ridha Soua, Pascale MinetAbstract:Micaz motes can communicate on multiple frequencies as specified in the 802.15.4 standard. This reality has given birth to Multichannel communication paradigm in Wireless Sensor Networks (WSNs). Obviously, Multichannel communication mitigates interferences, jamming and congestion, whereas it brings also challenging issues. Thus, in this paper, we are motivated to draw a picture of Multichannel assignment protocols in WSNs. After having identified the reasons of resorting to Multichannel communication paradigm in WSNs and the specific issues that should be tackled, we propose a classification of Multichannel assignment protocols, pointing out different channel selection policies, channel assignment methods and channel coordination techniques. We conclude by a recapitulative table including many examples of existing Multichannel protocols designed for WSNs.
Alexey Ozerov - One of the best experts on this subject based on the ideXlab platform.
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Multichannel audio declipping
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2016Co-Authors: Alexey Ozerov, Cagdas Bilen, Patrick PerezAbstract:Audio declipping consists in recovering so-called clipped audio samples that are set to a maximum / minimum threshold. Many different approaches were proposed to solve this problem in case of singlechannel (mono) recordings. However, while most of audio recordings are Multichannel nowadays, there is no method designed specifically for Multichannel audio declipping, where the inter-channel correlations may be efficiently exploited for a better declipping result. In this work we propose for the first time such a Multichannel audio declipping method. Our method is based on representing a Multichannel audio recording as a convolutive mixture of several audio sources, and on modeling the source power spectrograms and mixing filters by nonnegative tensor factorization model and full-rank covariance matrices, respectively. A generalized expectation-maximization algorithm is proposed to estimate model parameters. It is shown experimentally that the proposed Multichannel audio de-clipping algorithm outperforms in average and in most cases a state-of-the-art single-channel declipping algorithm applied to each channel independently.
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ICASSP - Multichannel audio declipping
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Alexey Ozerov, Cagdas Bilen, Patrick PerezAbstract:Audio declipping consists in recovering so-called clipped audio samples that are set to a maximum / minimum threshold. Many different approaches were proposed to solve this problem in case of singlechannel (mono) recordings. However, while most of audio recordings are Multichannel nowadays, there is no method designed specifically for Multichannel audio declipping, where the inter-channel correlations may be efficiently exploited for a better declipping result. In this work we propose for the first time such a Multichannel audio declipping method. Our method is based on representing a Multichannel audio recording as a convolutive mixture of several audio sources, and on modeling the source power spectrograms and mixing filters by nonnegative tensor factorization model and full-rank covariance matrices, respectively. A generalized expectation-maximization algorithm is proposed to estimate model parameters. It is shown experimentally that the proposed Multichannel audio de-clipping algorithm outperforms in average and in most cases a state-of-the-art single-channel declipping algorithm applied to each channel independently.
Ridha Soua - One of the best experts on this subject based on the ideXlab platform.
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Multichannel assignment protocols in wireless sensor networks
Pervasive and Mobile Computing, 2015Co-Authors: Ridha Soua, Pascale MinetAbstract:With the spectacular development in radio and MEMS technologies, having sensor nodes capable of efficiently tuning their frequency over different channels is becoming more and more straightforward. For instance, TelosB motes can communicate on multiple frequencies as specified in the 802.15.4 standard. This reality has given birth to the Multichannel communication paradigm in Wireless Sensor Networks (WSNs). While, Multichannel communication obviously mitigates interferences, jamming and congestion, it also raises a number of challenging issues. In this paper, we set out to present a picture of Multichannel assignment protocols in WSNs. After identifying the reasons of resorting to Multichannel communication paradigm in WSNs and the specific issues that should be tackled, we propose a classification of Multichannel assignment protocols, pointing out different channel selection policies, channel assignment categories and channel assignment methods. We conclude by a recapitulative table presenting many examples of existing Multichannel protocols designed for WSNs and highlight promising research directions.
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A survey on Multichannel assignment protocols in Wireless Sensor Networks
2011Co-Authors: Ridha Soua, Pascale MinetAbstract:Micaz motes can communicate on multiple frequencies as specified in the 802.15.4 standard. This reality has given birth to Multichannel communication paradigm in Wireless Sensor Networks (WSNs). Obviously, Multichannel communication mitigates interferences, jamming and congestion, whereas it brings also challenging issues. Thus, in this paper, we are motivated to draw a picture of Multichannel assignment protocols in WSNs. After having identified the reasons of resorting to Multichannel communication paradigm in WSNs and the specific issues that should be tackled, we propose a classification of Multichannel assignment protocols, pointing out different channel selection policies, channel assignment methods and channel coordination techniques. We conclude by a recapitulative table including many examples of existing Multichannel protocols designed for WSNs.