## Filtering frequencies from .m4a data

Question

I'm trying to analyze a sound file (.m4a) to get the amplitudes over time and make a graph. I found some code online that works great (below). However, I would like to additionally filter out all sounds that are not in a targeted frequency range. E.g. I want to only graph the sounds that are between 1900-2100 Hz. How can I do this?

```
var processingBuffer = [Float](repeating: 0.0, count: Int(readFile.arrayFloatValues.count))
let sampleCount = vDSP_Length(readFile.arrayFloatValues.count)
vDSP_vabs(readFile.arrayFloatValues, 1, &processingBuffer, 1, sampleCount);
let samplesPerPixel = 1
let filter = [Float](repeating: 1.0 / Float(samplesPerPixel), count: Int(samplesPerPixel))
let downSampledLength = Int(readFile.arrayFloatValues.count / samplesPerPixel)
var downSampledData = [Float](repeating:0.0, count:downSampledLength)
vDSP_desamp(processingBuffer,
vDSP_Stride(samplesPerPixel),
filter, &downSampledData,
vDSP_Length(downSampledLength),
vDSP_Length(samplesPerPixel))
readFile.points = downSampledData.map{CGFloat($0)}
```

**Edit**

The recording is actually recorded from the device's microphone at an earlier time. Is it perhaps easier to apply a filter at the recording stage?

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## Answers to Filtering frequencies from .m4a data ( 1 )

A possible way would be to apply a discrete fourier transformation to the sampled file. Fourier transformation transfers audio data from time-domain to frequency-domain. Once you have that data in frequency domain, you simply can "cut out" the frequency that you don't want to have and do a inverse Fourier transformation with your reduced data, so that you have it in time domain again and proceed with the code you mentioned. Have a look at https://github.com/christopherhelf/Swift-FFT-Example, it provides example code how to deal with swift and Fast Fourier transformation (FFT). Hope this gives you a direction.