NEURAL NETWORK-BASED LOUDSPEAKER MODELING WITH A DECONVOLUTION FILTER
First Claim
1. A computer-implemented method of generating a filter for a loudspeaker control system, the method comprising:
- receiving, via a sensor, a measured system output of a loudspeaker and a reverberant environment in which the loudspeaker is disposed;
extracting a head portion and tail portion from the measured system output, wherein the head portion includes a measured output of the loudspeaker and the tail portion includes a response of the reverberant environment;
determining an inverse of the response of the reverberant environment; and
generating the filter based on the inverse of the response.
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Abstract
A technique for controlling a loudspeaker system with an artificial neural network includes 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. The techniques further include 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.
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Citations
20 Claims
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1. A computer-implemented method of generating a filter for a loudspeaker control system, the method comprising:
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receiving, via a sensor, a measured system output of a loudspeaker and a reverberant environment in which the loudspeaker is disposed; extracting a head portion and tail portion from the measured system output, wherein the head portion includes a measured output of the loudspeaker and the tail portion includes a response of the reverberant environment; determining an inverse of the response of the reverberant environment; and generating the filter based on the inverse of the response. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. The method of claim I, wherein the measured system output comprises an acoustic signal having a final amplitude that is approximately 60 db less than an initial amplitude.
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12. 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:
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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. - View Dependent Claims (13, 16, 19, 20)
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- 14. The system of claim 14, wherein the response of the reverberant environment includes a response to an output of the loudspeaker while the loudspeaker is disposed in the reverberant environment.
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17. A system, comprising:
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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. - View Dependent Claims (18)
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Specification