Hybrid feedforward-feedback active noise reduction for hearing protection and communication.

A hybrid active noise reduction (ANR) architecture is presented and validated for a circumaural earcup and a communication earplug. The hybrid system combines source-independent feedback ANR with a Lyapunov-tuned leaky LMS filter (LyLMS) improving gain stability margins over feedforward ANR alone. In flat plate testing, the earcup demonstrates an overall C-weighted total noise reduction of 40 dB and 30-32 dB, respectively, for 50-800 Hz sum-of-tones noise and for aircraft or helicopter cockpit noise, improving low frequency (<100 Hz) performance by up to 15 dB over either control component acting individually.

Experimental evaluation of leaky least-mean-square algorithms for active noise reduction in communication headsets.

An adaptive leaky normalized least-mean-square (NLMS) algorithm has been developed to optimize stability and performance of active noise cancellation systems. The research addresses LMS filter performance issues related to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio. The adaptive leaky NLMS algorithm is based on a Lyapunov tuning approach in which three candidate algorithms, each of which is a function of the instantaneous measured reference input, measurement noise variance, and filter length, are shown to provide varying degrees of tradeoff between stability and noise reduction performance.