Neural network-based loudspeaker modeling with a deconvolution filter

A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, configure the one or more processors to control a loudspeaker system with an artificial neural network, by performing the steps of:

• filtering, with a deconvolution filter, a measured system response of a loudspeaker and a reverberant environment in which the loudspeaker is disposed to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment;
• generating, via a neural network model, an initial neural network output based on the audio input signal;
• comparing the initial neural network output to the filtered response to determine an error value;
• and
• generating, via the neural network model, an updated neural network output based on the audio input signal and the error value.

A system, comprising:

• a loudspeaker disposed in a reverberant environment;
• a memory storing a loudspeaker control algorithm;
• and
• one or more processors that are coupled to the memory and, when executing the loudspeaker control algorithm, are configured to:
• filter, with a deconvolution filter, a measured system response of the loudspeaker and the reverberant environment to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment;
• generate, via a neural network model, an initial neural network output based on the audio input signal;
• compare the initial neural network output to the filtered response to determine an error value;
• and
• generate, via the neural network model, an updated neural network output based on the audio input signal and the error value.