# Mel Frequency Cepstral Coefficients

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. 

## Usage
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)
```

## How it works

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);
```


## License

Licensed under either of

- Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or <http://www.apache.org/licenses/LICENSE-2.0>)
- MIT license ([LICENSE-MIT](LICENSE-MIT) or <http://opensource.org/licenses/MIT>)

at your option.

### Contribution

Unless you explicitly state otherwise, any contribution intentionally
submitted for inclusion in the work by you, as defined in the Apache-2.0
license, shall be dual licensed as above, without any additional terms or
conditions.