Neural network filtering techniques for compensating linear and non-linear distortion of an audio transducer

Abstract

Neural networks provide efficient, robust and precise filtering techniques for compensating linear and non-linear distortion of an audio transducer such as a speaker, amplified broadcast antenna or perhaps a microphone. These techniques include both a method of characterizing the audio transducer to compute the inverse transfer functions and a method of implementing those inverse transfer functions for reproduction. The inverse transfer functions are preferably extracted using time domain calculations such as provided by linear and non-linear neural networks, which more accurately represent the properties of audio signals and the audio transducer than conventional frequency domain or modeling based approaches. Although the preferred approach is to compensate for both linear and non-linear distortion, the neural network filtering techniques may be applied independently.