Nonlinear noise filtering and beamforming using the perceptron and its Volterra approximation

The multilayer perceptron, an artificial neural network, is applied to the problem of interference reduction in single- and multiple-sensor systems. The filter is able to operate approximately as a linear trapped delay line if nonlinear processing cannot further reduce the mean-squared error of the output. Supplanting the activation function of the perceptron by a polynomial leads to the finite-order Volterra filter for which optimum weights can be calculated. Preliminary examples using the perceptron in single-sensor noise filtering show output signal-to-noise ratio (SNR) improvements of up to 2.2 dB compared to the optimum linear filter. Experiments with a nonlinear two-microphone beamformer show a 2.7 dB SNR enhancement for a sinusoidal target and an off-axis white noise jammer. For speech inputs under anechoic conditions, the Volterra beamformer achieved an average intelligibility improvement of 5.7%.