Method for tuning an adaptive leaky LMS filter

• US 20030026438A1
• Filed: 06/22/2001
• Published: 02/06/2003
• Est. Priority Date: 06/22/2001
• Status: Active Grant

• Current Assignee: The Trustees of Dartmounth College (Dartmouth College)
• Original Assignee: The Trustees of Dartmounth College (Dartmouth College)
• Inventors: Collier, Robert Douglas, Cartes, David A., Ray, Laura R.
• Granted Patent: US 6,741,707 B2
• Time in Patent Office: Days
• US Class Current: 381/71.11
• CPC Class Codes: G10K 11/17854 the filter being an adaptive filter
• CPC Class Codes: G10K 11/17855 for improving speed or power requirements
• CPC Class Codes: G10K 11/17861 using additional means for damping sound, e.g. using sound absorbing panels
• CPC Class Codes: G10K 11/17881 the reference signal being an acoustic signal, e.g. recorded with a microphone

Abstract

A method to automatically and adaptively tune a leaky, normalized least-mean-square (LNLMS) algorithm so as to maximize the stability and noise reduction performance in feedforward adaptive noise cancellation systems. The automatic tuning method provides for time-varying tuning parameters λ_k and μ_k that are functions of the instantaneous measured acoustic noise signal, weight vector length, and measurement noise variance. The method addresses situations in which signal-to-noise ratio varies substantially due to nonstationary noise fields, affecting stability, convergence, and steady-state noise cancellation performance of LMS algorithms. The method has been embodied in the particular context of active noise cancellation in communication headsets. However, the method is generic, in that it is applicable to a wide range of systems subject to nonstationary, i.e., time-varying, noise fields, including sonar, radar, echo cancellation, and telephony.

Claims

1. A method of tuning an adaptive feedforward noise cancellation algorithm, comprising the acts of:

providing a feedforward LMS tuning algorithm including at least first and second time varying parameters; and

adjusting said at least first and second time varying parameters as a function of instantaneous measured acoustic noise, a weight vector length and measurement noise variance.

2. A method of claim 1 wherein said feedforward LMS tuning algorithm includes the formulas:

y_k=W_k^TX_k W_k+1=λ

_kW_k+μ

_kX_ke_k

3. The method of claim 2 wherein said time varying parameters include:

4. The method of claim 2 wherein said acts of adjusting said at least first and second time varying parameters includes utilizing the formulas:

5. The method of tuning an algorithm for providing noise cancellation, comprising the acts of:

receiving a measured reference signal, the measured reference signal including a measurement noise component having a measurement noise value of known variance; and

generating an acoustic noise cancellation signal according to the formulas;

y_k=W_k^TX_k W_k+1=λ

_kW_k+μ

_kX_ke_k

wherein time varying parameters λ

_k and μ

_k are determined according to the formulas;

${\mathrm{\mu <br><br>}}_k=\frac{{\mathrm{\mu <br><br>}}_o<br><br>{\mathrm{\lambda <br><br>}}_k}{{\left(X_k+Q_k\right)}^T<br><br>\left(X_k+Q_k\right)}$${\mathrm{\lambda <br><br>}}_k=\frac{{\left(X_k+Q_k\right)}^T<br><br>\left(X_k+Q_k\right)-2<br><br>L<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\sigma <br><br>}}_q^2}{{\left(X_k+Q_k\right)}^T<br><br>\left(X_k+Q_k\right)}$

wherein X_k+Q_k is a measured reference signal;

Q_k is electronic noise and quantization;

σ

_q² is a known variance of the measurement noise; and

L is the length of weight vector W_k.