Method for tuning an adaptive leaky LMS filter

Details Method for tuning an adaptive leaky LMS filter Method for tuning an adaptive leaky LMS filter

2001-06-22

2004-05-25

Harvey, Minsun Oh (Department: 2644)

Electrical audio signal processing systems and devices

Acoustical noise or sound cancellation

Counterwave generation control path

C381S071600

Reexamination Certificate

active

06741707

ABSTRACT:

FIELD OF THE INVENTION
The present invention relates to a method for automatically and adaptively tuning a leaky, normalized least-mean-square (LMS) algorithm so as to maximize the stability and noise reduction performance of feedforward adaptive noise cancellation systems and to eliminate the need for ad-hoc, empirical tuning.
SUMMARY OF THE INVENTION
Noise cancellation systems are used in various applications ranging from telephony to acoustic noise cancellation in communication headsets. There are, however, significant difficulties in implementing such stable, high performance noise cancellation systems.
In the majority of adaptive systems, the well-known LMS algorithm is used to perform the noise cancellation. This algorithm, however, lacks stability in the presence of inadequate excitation, non-stationary noise fields, low signal-to-noise ratio, or finite precision effects due to numerical computations. This has resulted in many variations to the standard LMS algorithm, none of which provide satisfactory performance over a range of noise parameters.
Among the variations, the leaky LMS algorithm has received significant attention. The leaky LMS algorithm, first proposed by Gitlin et al. introduces a fixed leakage parameter that improves stability and robustness. However, the leakage parameter improves stability at a significant expense to noise reduction performance.
Thus, the current state-of-the-art LMS algorithms must tradeoff stability and performance through manual selection of tuning parameters, such as the leakage parameter. In such noise cancellation systems, a constant, manually selected tuning parameter cannot provide optimized stability and performance for a wide range of different types of noise sources such as deterministic, tonal noise, stationary random noise, and highly nonstationary noise with impulsive content, nor adapt to highly variable and large differences in the dynamic ranges evident in real-world noise environments. Hence, “worst case”, i.e., highly variable, nonstationary noise environment scenarios must be used to select tuning parameters, resulting in substantial degradation of noise reduction performance over a full range of noise fields.


REFERENCES:
patent: 5627896 (1997-05-01), Southward
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