Abstract
As speech enhancement concerned, blind speech deconvolution are widely used in speech dereverberation, automatic speech recognition and hearing aid designs. it is generally a single-input multiple-output (SIMO) system identification followed by a signal deconvolution. A novel approach, in which blind multichannel identifications of a SIMO system and its inverse system are performed concurrently with Kalman filter, is proposed in this paper. Speech deconvolution is the direct result of the algorithm. The state vector of the Kalman filter is composed of the multichannel impulse responses (MCIRs) of the SIMO system and its inverse system; the measurement equation of the Kalman filter is constructed with the cross relation (CR) among the observed signals and the input-output relation by feeding the deconvolved signal into the identified SIMO system. Comparing with traditional deconvolution methods, the proposed approach is more noise robust and computationally efficient.
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Mei, T., He, G., Zhao, Y. et al. Blind identification of the inverse of SIMO system and deconvolution with Kalman filter. Int J Speech Technol 25, 975–986 (2022). https://doi.org/10.1007/s10772-022-10000-1
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DOI: https://doi.org/10.1007/s10772-022-10000-1