A common pre-processing step in Machine Learning with audio signals is the application of a Mel Frequency Cepstral Coefficients (MFCC) transformation. They compress the signal to a very small number of coefficients (around 16 for every 10ms) and decorrelates the signal to express only the transmission function (e.g. only the formants of a utterance not the pitch). This makes them very popular in Automatic Speech Recognition (ASR), Room Classification, Speaker Recognition etc.
Add this to your Cargo.toml
toml
[dependencies]
mfcc = "0.1"
The library can use two different FFT libraries. Either use rustfft (a pure rust FFT implementation) with the standard feature fftrust or use fftw (a popular FFT library) with
toml
[dependencies.mfcc]
version = "0.1"
default-features = false
features = ["fftextern"]
A rough benchmark shows that their performance are comparable, for FFTW:
test tests::bench_mfcc ... bench: 123,959 ns/iter (+/- 22,979)
For rustfft:
test tests::bench_mfcc ... bench: 162,603 ns/iter (+/- 35,914)
First you need to segment you audio data in chunks of around 10ms-20ms (max 1024 samples for 48kHz). From these you can calculate the MFCC coefficients with ```rust use mfcc::Transform;
let mut state = Transform::new(48000, 1024); let mut output = vec![0.0; 16*3];
state.transform(&input, &mut output); ```
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