Method and device for updating coefficient vector of finite impulse response filter

Patent metadata

Field	Value
Publication number	US-11450335-B2
Application number	US-201916978644-A
Country	US
Kind code	B2
Filing date	Feb 27, 2019
Priority date	Mar 9, 2018
Publication date	Sep 20, 2022
Grant date	Sep 20, 2022

Abstract

A method and a device for updating a coefficient vector of a finite impulse response filter are provided. The update method includes: obtaining an updated step-size diagonal matrix for a coefficient vector of the FIR filter; and obtaining an updated coefficient vector of the FIR filter based on the updated step-size diagonal matrix.

First claim

What is claimed is:

1. A method for updating a coefficient vector of a finite impulse response (FIR) filter, comprising:
obtaining an updated step-size diagonal matrix for a coefficient vector of the FIR filter; and
obtaining, based on the updated step-size diagonal matrix, an updated coefficient vector of the FIR filter;
wherein the obtaining the updated step-size diagonal matrix for the coefficient vector of the FIR filter comprises: updating, based on an end moment of pre-learning and a pre-defined updating period, a step-size diagonal matrix used to update the coefficient vector of the FIR filter, to obtain the updated step-size diagonal matrix;
wherein the updating, based on the end moment of the pre-learning and the pre-defined updating period, the step-size diagonal matrix used to update the coefficient vector of the FIR filter, to obtain the updated step-size diagonal matrix, comprises:
obtaining, a coefficient vector of the FIR filter at an updating moment of the step-size diagonal matrix;
obtaining, based on the coefficient vector of the FIR filter at the updating moment of the step-size diagonal matrix, an estimated value of an attenuation factor of a step-size diagonal matrix of an exponentially weighted step-size normalized least mean square (ES-NLMS) algorithm; and
obtaining, based on the estimated value of the attenuation factor, an updated step-size diagonal matrix of the ES-NLMS algorithm at the updating moment of the step-size diagonal matrix.

2. The method according to claim 1 , wherein in a case that the updating moment is the end moment of the pre-learning, the obtaining the coefficient vector of the FIR filter at the updating moment of the step-size diagonal matrix comprises: obtaining, the coefficient vector of the FIR filter at the end moment of the pre-learning, according to the following formula

h
→

(

k
+
1

)

=

h
→

(
k
)

+

α
⁢

e
⁡
(
k
)

x
→

T

(
k
)

·

x
→

(
k
)

+

δ
⁡
(
k
)

⁢

x
→

(
k
)

;

wherein {right arrow over (h)}(k+1) is the coefficient vector of the FIR filter at a (k+1) th moment; {right arrow over (h)}(k) is a coefficient vector of the FIR filter at a k th moment; α is a learning rate constant, and 0<α<2; e(k) is an error signal at the k th moment; {right arrow over (x)}(k) is a far end received signal vector, {right arrow over (x)}(k)=[x(k), x(k−1), . . . , x(k−L+1)] T , x(k−n) is a far end received signal at a (k−n) th moment, n=0, 1, . . . , L−1, L is the quantity of coefficients of the filter, and T is a transpose operator; e(k)=y(k)−ŷ(k)+n(k); y(k) is an echo signal; ŷ(k) is an estimation of the echo signal; n(k) is an ambient noise signal received by a microphone; δ(k) is a time-varying regularization factor; (k+1) is a time index of a signal sample at the end moment of the pre-learning.

3. The method according to claim 1 , wherein in a case that the updating moment is a moment corresponding to the pre-defined updating period of the step-size diagonal matrix after the pre-learning is ended, the obtaining the coefficient vector of the FIR filter at the updating moment of the step-size diagonal matrix comprises: obtaining, the coefficient vector of the FIR filter at the moment corresponding to the pre-defined updating period of the step-size diagonal matrix after the pre-learning is ended, according to the following formula

h
→

(

k
+
1

)

=

h
→

(
k
)

+

A
^

⁢

e
⁡
(
k
)

x
→

T

(
k
)

·

x
→

(
k
)

+

δ
⁡
(
k
)

⁢

x
→

(
k
)

;

wherein {right arrow

Assignees

• China Academy Telecommunications Technology
• Datang mobile communications equipment co ltd

Inventors

• Zhou Xuan
• Liang Min

Classifications

• G10L21/0232 Primary

  Processing in the frequency domain · CPC title

• G06F30/23

  using finite element methods [FEM] or finite difference methods [FDM] · CPC title

• G10L25/51

  for comparison or discrimination · CPC title

• G10L25/21

  the extracted parameters being power information · CPC title

• H04M9/082

  using echo cancellers (echo cancellers per se H04B3/23) · CPC title