From a technical/algorithmic POV - this doesn't sound particularly remarkable, because it sounds about the same as how modern CPU's "boost" themselves.
And actually, I think there is similar tech in "speaker systems" already. An "older" one that I've read about is from RCF (an old+big Italian company with systems ranging from desktop to festival size), and they call it [Bass Motion Control](https://www.rcf.it/en/art-9-series);
> The BMC method works by creating a complete map of the dynamic behavior of the woofer, to generate a custom algorithm that only limits over-excursions. This gives total freedom of signal reproduction to the transducer. When high-pass filters normally protect the woofer motion from becoming destructive but change the phase behavior, the new BMC algorithm breaks conventional rules.
Now I don't know how effective RCF's approach truly is, but another company that is doing "big-things" is Dirac. They released a blog post about a year ago titled [Boosting Audio System Sustainability with Dirac](https://www.dirac.com/blog/boosting-audio-system-sustainabil...), and there is a section called Enhancing performance with optimized components;
> By employing Long Short-Term Memory (LSTM) neural networks, we can make the driving force on the voice coil (the part of the speaker that turns electricity into sound) more consistent, improving the mechanical design and compensating for magnetic limitations.
> NLC adjusts the voice coil current to correct force factor irregularities (inconsistencies in the voice coil’s efficiency) without requiring complex mechanical measurements. In tests with an otherwise suboptimal driver, our technology reduced distortion by 10 dB, nearly matching the performance of a well-designed driver.
And actually, I think there is similar tech in "speaker systems" already. An "older" one that I've read about is from RCF (an old+big Italian company with systems ranging from desktop to festival size), and they call it [Bass Motion Control](https://www.rcf.it/en/art-9-series);
> The BMC method works by creating a complete map of the dynamic behavior of the woofer, to generate a custom algorithm that only limits over-excursions. This gives total freedom of signal reproduction to the transducer. When high-pass filters normally protect the woofer motion from becoming destructive but change the phase behavior, the new BMC algorithm breaks conventional rules.
Now I don't know how effective RCF's approach truly is, but another company that is doing "big-things" is Dirac. They released a blog post about a year ago titled [Boosting Audio System Sustainability with Dirac](https://www.dirac.com/blog/boosting-audio-system-sustainabil...), and there is a section called Enhancing performance with optimized components;
> By employing Long Short-Term Memory (LSTM) neural networks, we can make the driving force on the voice coil (the part of the speaker that turns electricity into sound) more consistent, improving the mechanical design and compensating for magnetic limitations.
> NLC adjusts the voice coil current to correct force factor irregularities (inconsistencies in the voice coil’s efficiency) without requiring complex mechanical measurements. In tests with an otherwise suboptimal driver, our technology reduced distortion by 10 dB, nearly matching the performance of a well-designed driver.