SoundGirls.org: The use of FIR filters (or finite impulse response filters) has grown in popularity in the live sound world as digital signal processing (DSP) for loudspeakers becomes more and more sophisticated. While not a new technology in itself, these filters provide a powerful tool in system optimization due to their linear phase properties. But what exactly do we mean by “finite impulse response” and how do these filters work? In order to understand digital signal processing better we are going to need to take a step back into our understanding of mathematics and levels of abstraction.
1 comment:
This is a really in depth article about FIR filters, but I have to say this is a LOT to tackle, even at a high level, in a single blog post. Digital signal processing is an incredibly dense (but beautiful) field, and if I'm being completely honest I think the article spends too much time trying to explain the math that quite frankly won't be used by anyone reading the article. In contrast, the example the author paraphrases from Lyons textbook about "averaging the number of cars crossing over a bridge" is a far more effective way of communicating the ideas of FIR filters, which I am glad was included. Heck, the inclusion of discrete convolutions caught me way off guard, since it is a somewhat complex topic in electrical engineering and signal processing. Even though it is complex, however, there are many visual ways to describe convolutions visually if you take a look at the wiki page for them that aren't offered here. Again, the math is cool and all, but when its taking up 2/3rds of the article and the last third is the real application, I think it might be trying to do too much (and that's coming from a guy who definitely does way too much math).
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