INVERSE FILTERING OF A LOUDSPEAKER AND ROOM ACOUSTICS USING TIME-DELAY NEURAL NETWORKS

Abstract

This paper presents the design of a neural-based acoustic control used for the equalization of the response of a sound reproduction system. The system usually can be modeled as a composite system of a loudspeaker and an acoustic signal-transmission channel. Generally, an acoustic signal radiated inside a room is linearly distorted by wall reflections. However, in a loudspeaker, the nonlinearity in the suspension system produces a significant distortion at low frequencies and the inhomogeneity in the flux density causes a nonlinear distortion at large output signals. Both the linear and nonlinear distortions should be reduced so that high fidelity sound can be reproduced However, the traditional adaptive equalizer which is only capable of dealing with linear systems or specific nonlinear systems cannot compensate these nonlinear distortions. The time-delay feedforward neural network (TDNN) which has the capability to learn arbitrary nonlinearity and process the temporal audio patterns are particularly recognized as the best nonlinear inverse filter of the composite system. The performance of a TDNN-based acoustic controller is verified by some simulation results.