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"Feedback" you say?

A project log for Good sound from bad

Lets use machine learning to make a bad speaker make good sound.

oliverOliver 08/20/2014 at 20:410 Comments

So I've got a few questions about my idea involving "feedback", which is normally a bad thing in the audio world.

Thats not always the case, and in fact it's very well researched.  If you want to learn more, look here:

http://en.wikipedia.org/wiki/Nyquist_stability_criterion

In fact...   Don't...   Because thats one of those Wikipedia pages that's only good if you already have a PhD in the topic...   Also, all that maths only applies to linear systems, which is the very assumption we don't want to make about our speaker!

The summary is this though:   When you thing you have figured out what the speakers doing (ie. for a given input signal, you have figured out what sound it should make, and what sound it really makes, and hence the 'error'), if you apply the inverse signal to cancel it out, and the problem gets worse instead of better, then you've fallen foul of the Nyquist stability criterion.

Luckily, *for a given frequency*, if this happens and one instead adds instead of subtracts the error, you're guaranteed to end up with a stable system...   Magic...

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