Compensation of Loudspeaker Nonlinearities with Deep Neural Networks

Abstract

Loudspeakers generate sound waves from electrical audio signals. They are inherently nonlinear as their performance varies when using small and high amplitude signals. Small loudspeakers or micro speakers, found in consumer electronics, are particularly sensitive to sound degrading nonlinearities or distortion at high playback volumes. This thesis proposes a compensation method based on deep neural networks (DNNs). A DNN based compensation model is trained to pre-compensate the loudspeaker input to reduce distortion. The compensation model learns to modify the input signal of a DNN based nonlinear loudspeaker model such that the nonlinear model output minimizes error to the output of a linear regression loudspeaker model. Both loudspeaker models are trained on monaural data recorded from a laptop micro speaker. The compensation model successfully reduces nonlinearities in simulation. Practical experiments, where compensated audio is played and recorded from the laptop speaker, show that the amount of nonlinearities is decreased. Informal listening of the recordings suggest that the compensation slightly alters some elements of the loudspeaker output sound.