公开(公告)号：US11567162B2

公开(公告)日：2023-01-31

申请号：US16664373

申请日：2019-10-25

Applicant: Huawei Technologies Co., Ltd.

Inventor： Kainan Chen ,  Jürgen Geiger ,  Mohammad Taghizadeh ,  Peter Grosche

IPC: G01S3/808 ,  G01S3/80 ,  H04R3/00 ,  G10L17/00

Abstract: A device for estimating Direction of Arrival (DOA) of sound from Q≥1 sound sources is provided. The device is configured to obtain a phase difference matrix, which includes measured phase difference values, each of the measured phase difference values being a measured value of a phase difference between two microphone units for a frequency bin in a range of frequencies of the sound. The device is further configured to generate a replicated phase difference matrix by replicating the measured phase difference values to other potential sinusoidal periods, calculate a DOA value for each phase difference value in the replicated phase difference matrix, and determine, as Q DOA results, the Q most prominent peak values in a histogram generated based on the calculated DOA values.

公开(公告)号：US11467244B2

公开(公告)日：2022-10-11

申请号：US16858208

申请日：2020-04-24

Applicant: Huawei Technologies Co., Ltd.

Inventor： Kainan Chen ,  Wenyu Jin

IPC: G01S3/80 ,  G01S3/808

Abstract: A device estimates direction of arrival (DOA) of sound from sound sources received by P microphones, wherein P≥>1. The device is configured to transform the output signals of the microphones into the frequency domain and compute a covariance matrix for each of N frequency bins in a range of frequencies of the sound. Further, the device is configured to calculate an adapted covariance matrix from each of the covariance matrices for wide-band merging, calculate an accumulated covariance matrix from the N adapted covariance matrices, and estimate the DOA for each of the sound sources based on the accumulated covariance matrix. In order to calculate an adapted covariance matrix from a covariance matrix, the device is configured to spectrally decompose the covariance matrix and obtain a plurality of eigenvectors, rotate each obtained eigenvector, and construct each rotated eigenvector back to the shape of the covariance matrix.