公开(公告)号：US20180270573A1

公开(公告)日：2018-09-20

申请号：US15940635

申请日：2018-03-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yue LANG ,  Wenyu JIN ,  Thomas SHERSON ,  Richard HEUSDENS ,  Willem Bastiaan KLEIJN

IPC: H04R3/00 ,  H04R1/40

CPC classification number: H04R3/005 ,  G10L21/0208 ,  G10L21/0232 ,  G10L2021/02166 ,  H04R1/406 ,  H04R2201/401 ,  H04R2420/07

Abstract: A sound processing node for an arrangement of sound processing nodes is disclosed. The sound processing nodes being configured to receive a plurality of sound signals, wherein the sound processing node comprises a processor configured to determine a beamforming signal on the basis of the plurality of sound signals weighted by a plurality of weights, wherein the processor is configured to determine the plurality of weights using a transformed version of a linearly constrained minimum variance approach, the transformed version of the linearly constrained minimum variance approach being obtained by applying a convex relaxation to the linearly constrained minimum variance approach.

公开(公告)号：US20190273987A1

公开(公告)日：2019-09-05

申请号：US16418363

申请日：2019-05-21

Applicant: Huawei Technologies Co., Ltd.

Inventor： Wenyu JIN ,  Thomas SHERSON ,  Willem Bastiaan KLEIJN ,  Richard HEUSDENS ,  Yue LANG

IPC: H04R3/00 ,  H04R1/40

Abstract: The invention relates to a sound processing node for an arrangement of sound processing nodes, the sound processing nodes being configured to receive a plurality of sound signals, wherein the sound processing node comprises a processor configured to generate an output signal on the basis of the plurality of sound signals weighted by a plurality of beamforming weights, wherein the processor is configured to adaptively determine the plurality of beamforming weights on the basis of an adaptive linearly constrained minimum variance beamformer using a transformed version of a least mean squares formulation of a constrained gradient descent approach, wherein the transformed version of the least mean squares formulation of the constrained gradient descent approach is based on a transformation of the least mean squares formulation of the constrained gradient descent approach to the dual domain.