A technique for estimating a set of parameter values for a lumped parameter model of a loudspeaker. The technique includes determining, via a neural network model, a first set of parameter values for the LPM of the loudspeaker based on an audio input signal and a first measured response of a loudspeaker that corresponds to the audio input signal. The technique further includes generating a first LPM response based on the first set of parameter values and comparing the first LPM response to the first measured response of the loudspeaker to determine a first error value. The technique further includes generating, via the neural network model, a second set of parameter values for the LPM of the loudspeaker based on the first error value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, configure the one or more processors to estimate a set of parameter values for a lumped parameter model (LPM) of a loudspeaker, by performing the steps of: determining, via a neural network model, a first set of parameter values for the LPM of the loudspeaker based on an audio input signal and a first measured response of a loudspeaker that corresponds to the audio input signal; generating a first LPM response based on the first set of parameter values; comparing the first LPM response to the first measured response of the loudspeaker to determine a first error value; and generating, via the neural network model, a second set of parameter values for the LPM of the loudspeaker based on the first error value. 2. The non-transitory computer-readable storage medium of claim 1, wherein the instructions further configure the one or more processors to perform the step of updating one or more neural network parameters of the neural network model based on the first error value, wherein the second set of parameter values is generated based on the one or more neural network parameters that have been updated. 3. The non-transitory computer-readable storage medium of claim 2, wherein the instructions further configure the one or more processors to perform the steps of: generating a second LPM response based on the second set of parameter values; comparing the second LPM response to a second measured response of the loudspeaker to determine a second error value; and generating, via the neural network model, a third set of parameter values for the LPM of the loudspeaker based on the second error value. 4. The non-transitory computer-readable storage medium of claim 3, wherein the instructions further configure the one or more processors to perform the steps of: determining a rate of change based on the first error value and the second error value; and prior to generating the third set of parameter values, updating the one or more neural network parameters of the neural network model based on the rate of change. 5. The non-transitory computer-readable storage medium of claim 4, wherein the one or more neural network parameters are updated based on a gradient descent algorithm. 6. The non-transitory computer-readable storage medium of claim 1, wherein the instructions further configure the one or more processors to perform the steps of: generating a second LPM response based on the second set of parameter values; comparing the second LPM response to a second measured response of the loudspeaker to determine a second error value; determining that the second error value is below a threshold error value; and in response, terminating execution of the neural network model for a specified period of time. 7. The non-transitory computer-readable storage medium of claim 6, wherein the instructions further configure the one or more processors to perform the steps of: resuming execution of the neural network model after the specified period of time; generating a third LPM response based on the audio input signal and the second set of parameter values; comparing the third LPM response to a third measured response of the loudspeaker to determine a third error value; determining that the third error value is above the threshold error value; in response, updating one or more neural network parameters of the neural network model based on the third error value; and generating, via the updated neural network model, a third set of parameter values for the LPM of the loudspeaker. 8. The non-transitory computer-readable storage medium of claim 6, wherein the second error value is based on a difference between the second LPM response and the second measured response of the loudspeaker, and the threshold error value comprises a predetermined percentage of one of the second LPM response and the second measured response of the loudspeaker. 9. The non-transitory computer-readable storage medium of claim 1, wherein the first measured response of the loudspeaker comprises at least one of a current through a voice coil of the loudspeaker, a displacement of a cone of the loudspeaker, and a sound pressure level. 10. A method for estimating a set of parameter values for a lumped parameter model (LPM) of a loudspeaker, the method comprising: determining, via a neural network model, a first set of parameter values for the LPM of the loudspeaker based on an audio input signal and a first measured response of a loudspeaker that corresponds to the audio input signal; generating a first LPM response based on the first set of parameter values; comparing the first LPM response to the first measured response of the loudspeaker to determine a first error value; training the neural network model based on the first error value, the audio input signal, and at least one measured response of the loudspeaker; and generating, via the trained neural network model, a second set of parameter values for the LPM of the loudspeaker. 11. The method of claim 10, wherein training the neural network model comprises updating one or more neural network parameters based on the first error value, and the second set of parameter values is generated based on the one or more neural network parameters that have been updated. 12. The method of claim 10, further comprising, for each of a plurality of training iterations: generating a LPM response based on a set of parameter values and the audio input signal; comparing the LPM response to a measured response of the loudspeaker to determine an error value; updating one or more neural network parameters of the neural network model based on the error value; and modifying, via the updated neural network model, at least one parameter value included in the set of parameter values. 13. The method of claim 10, further comprising further training the neural network model by: generating a second LPM response based on the second set of parameter values; comparing the second LPM response to a second measured response of the loudspeaker to determine a second error value; determining a rate of change based on the first error value and the second error value; updating one or more neural network parameters of the neural network model based on the rate of change; and generating, via the updated neural network model, a third set of parameter values for the LPM of the loudspeaker. 14. The method of claim 13, wherein the one or more neural network parameters are updated based on a gradient descent algorithm. 15. The method of claim 10, further comprising: generating a second LPM response based on the second set of parameter values; and comparing the second LPM response to a second measured response of the loudspeaker to determine a second error value. 16. A system for estimating a set of parameter values for a lumped parameter model (LPM) of a loudspeaker, the system comprising: a memory storing a loudspeaker parameter estimation application; and one or more processors that are coupled to the memory and, when executing the loudspeaker parameter estimation application, are configured to: determine, via a neural network model, a first set of parameter values for the LPM of the loudspeaker based on an audio input signal and a first measured response of a loudspeaker that corresponds to the audio input signal; generate a first LPM response based on the first set of parameter values; train the neural network model based on the first LPM response and the first measured response of the loudspeaker; and generate, via the trained neural network model, a second set of parameter values for the LPM of the loudspeaker. 17. The system of claim 16, wherein training the neural network model comprises computing an error value based on the first LPM response and the first measured response of the loudspeaker. 18. The system of claim 16, wherein the one or more processors are further configured to further train the neural network model by: generating a second LPM response based on the second set of parameter values; comparing the second LPM response to a second measured response of the loudspeaker to determine an error value; determining that the error value exceeds a threshold error value; in response, updating one or more neural network parameters of the neural network model; and generating, via the updated neural network model, a third set of parameter values for the LPM of the loudspeaker based on the one or more neural network parameters that have been updated. 19. The system of claim 18, wherein the one or more neural network parameters are updated based on a gradient descent algorithm. 20. The system of claim 16, wherein the first measured response of the loudspeaker comprises at least one of a current through a voice coil of the loudspeaker, a displacement of a cone of the loudspeaker, and a sound pressure level.