@InProceedings{8552954,
  author = {W. Xue and A. H. Moore and M. Brookes and P. A. Naylor},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Modulation-Domain Parametric Multichannel Kalman Filtering for Speech Enhancement},
  year = {2018},
  pages = {2509-2513},
  abstract = {The goal of speech enhancement is to reduce the noise signal while keeping the speech signal undistorted. Recently we developed the multichannel Kalman filtering (MKF) for speech enhancement, in which the temporal evolution of the speech signal and the spatial correlation between multichannel observations are jointly exploited to estimate the clean signal. In this paper, we extend the previous work to derive a parametric MKF (PMKF), which incorporates a controlling factor to achieve the trade-off between the speech distortion and noise reduction. The controlling factor weights between the speech distortion and noise reduction related terms in the cost function of PMKF, and based on the minimum mean squared error (MMSE) criterion, the optimal PMKF gain is derived. We analyse the performance of the proposed PMKF and show the differences with the speech distortion weighted multichannel Wiener filter (SDW-MWF). We conduct experiments in different noisy conditions to evaluate the impact of the controlling factor on the noise reduction performance, and the results demonstrate the effectiveness of the proposed method.},
  keywords = {Kalman filters;least mean squares methods;mean square error methods;speech enhancement;Wiener filters;factor weights;speech signal;noise reduction performance;speech distortion weighted multichannel Wiener filter;PMKF;parametric MKF;clean signal;multichannel observations;noise signal;speech enhancement;modulation-domain parametric multichannel Kalman filtering;Distortion;Noise reduction;Speech enhancement;Noise measurement;Estimation;Cost function;Speech enhancement;Microphone arrays;Kalman filtering;Modulation domain},
  doi = {10.23919/EUSIPCO.2018.8552954},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438011.pdf},
}

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Recently we developed the multichannel Kalman filtering (MKF) for speech enhancement, in which the temporal evolution of the speech signal and the spatial correlation between multichannel observations are jointly exploited to estimate the clean signal. In this paper, we extend the previous work to derive a parametric MKF (PMKF), which incorporates a controlling factor to achieve the trade-off between the speech distortion and noise reduction. The controlling factor weights between the speech distortion and noise reduction related terms in the cost function of PMKF, and based on the minimum mean squared error (MMSE) criterion, the optimal PMKF gain is derived. We analyse the performance of the proposed PMKF and show the differences with the speech distortion weighted multichannel Wiener filter (SDW-MWF). We conduct experiments in different noisy conditions to evaluate the impact of the controlling factor on the noise reduction performance, and the results demonstrate the effectiveness of the proposed method.","keywords":"Kalman filters;least mean squares methods;mean square error methods;speech enhancement;Wiener filters;factor weights;speech signal;noise reduction performance;speech distortion weighted multichannel Wiener filter;PMKF;parametric MKF;clean signal;multichannel observations;noise signal;speech enhancement;modulation-domain parametric multichannel Kalman filtering;Distortion;Noise reduction;Speech enhancement;Noise measurement;Estimation;Cost function;Speech enhancement;Microphone arrays;Kalman filtering;Modulation domain","doi":"10.23919/EUSIPCO.2018.8552954","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438011.pdf","bibtex":"@InProceedings{8552954,\n author = {W. Xue and A. H. Moore and M. Brookes and P. A. Naylor},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Modulation-Domain Parametric Multichannel Kalman Filtering for Speech Enhancement},\n year = {2018},\n pages = {2509-2513},\n abstract = {The goal of speech enhancement is to reduce the noise signal while keeping the speech signal undistorted. Recently we developed the multichannel Kalman filtering (MKF) for speech enhancement, in which the temporal evolution of the speech signal and the spatial correlation between multichannel observations are jointly exploited to estimate the clean signal. In this paper, we extend the previous work to derive a parametric MKF (PMKF), which incorporates a controlling factor to achieve the trade-off between the speech distortion and noise reduction. The controlling factor weights between the speech distortion and noise reduction related terms in the cost function of PMKF, and based on the minimum mean squared error (MMSE) criterion, the optimal PMKF gain is derived. We analyse the performance of the proposed PMKF and show the differences with the speech distortion weighted multichannel Wiener filter (SDW-MWF). We conduct experiments in different noisy conditions to evaluate the impact of the controlling factor on the noise reduction performance, and the results demonstrate the effectiveness of the proposed method.},\n keywords = {Kalman filters;least mean squares methods;mean square error methods;speech enhancement;Wiener filters;factor weights;speech signal;noise reduction performance;speech distortion weighted multichannel Wiener filter;PMKF;parametric MKF;clean signal;multichannel observations;noise signal;speech enhancement;modulation-domain parametric multichannel Kalman filtering;Distortion;Noise reduction;Speech enhancement;Noise measurement;Estimation;Cost function;Speech enhancement;Microphone arrays;Kalman filtering;Modulation domain},\n doi = {10.23919/EUSIPCO.2018.8552954},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438011.pdf},\n}\n\n","author_short":["Xue, W.","Moore, A. H.","Brookes, M.","Naylor, P. 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