I don’t know either, but here is a link.

https://www.minidsp.com/applications/dsp-basics/fir-vs-iir-filtering
**Infinite impulse response (IIR) filters**
IIR filters are the most efficient type of filter to implement in DSP (digital signal processing). They are usually provided as "biquad" filters. For example, in the parametric EQ block of a miniDSP plugin, each peak/notch or shelving filter is a single biquad. In the crossover blocks, each crossover uses up to 4 biquads. Each band of a graphic EQ is a single biquad, so a full 31-band graphic EQ uses 31 biquads per channel.

The amount of processing that is required to compute a biquad is relatively small. This is what enables the low-cost miniDSP products to implement a full active crossover with parametric EQ on all input and output channels. The DSP (digital signal processor) on each board can compute a certain number of biquads, and this is the primary thing that determines how many filters are available in each plugin.

The miniDSP biquads can be programmed using the crossover parameters (slope and frequency),the parametric filter parameters (center frequency, gain, and Q),and so on. They can also be programmed with custom filter shapes by directly entering the biquad coefficients - five numbers that are used to compute the biquad output from its input. You can generate these coefficients by using the community-contributed

custom biquad programming spreadsheet.

**Finite impulse response (FIR) filters**
An FIR filter requires more computation time on the DSP and more memory. The DSP chip therefore needs to be more powerful. miniDSP products that support FIR filtering include the

OpenDRC and the

miniSHARC kit.

FIR filters are specified using a large array of numbers. In the case of the OpenDRC, there are 6144 coefficients (or "taps") per channel. In the case of the miniSHARC, there are a total of 10240 taps assignable to all input and output channels. Generation of this large array of numbers must be done in a separate program, such as

rephase,

Acourate, and others.

**FIR** filtering has these advantages over IIR filtering:

- It can implement linear-phase filtering. This means that the filter
**has no phase shift across the frequency band**. Alternately, the phase can be corrected independently of the amplitude. See examples below.
- It can be used to correct frequency-response errors in a loudspeaker to a
**finer degree of precision than using IIR**s.

However, FIRs can be limited in resolution at low frequencies, and the success of applying FIR filters depends greatly on the program that is used to generate the filter coefficients. Usage is generally more complicated and time-consuming than IIR filters.