
This module have functions to compute DFT using the correlation algorithm
(the simplest and easy to understand, also the slowest)
> Various methods are used to obtain DFT for time domain samples including use
of Simultaneous Equations using Gaussian elimination, correlation, and using
the Fast Fourier Transform algorithm. The first option requires massive work
even for a comparitively small number of samples. In actual practice,
correlation is the preferred method if the DFT has less than about 32 points.
The functions of this module are not intended to be used in production. It
has two objectives:
- Educational: learn how to implement the DFT correlation algorithm
- Test: test more complex algorithms against this to check results
This is part of dsp-kit
Example
``js`
var dft = require('dsp-dft')
dft.forward(signal)
* dft
* ~dft(src, dest, inverse) ⇒ Object
* ~forward(signal) ⇒ Object
* ~inverse(signal) ⇒ Array
It accepts real and complex signals of any size.
It implements the mathematical function as it, without any kind of optimization,
so it's the slowest algorithm possible.
This algorithm is not intended to be used in production. It's main use
(apart from the educational purposes) is to check the output of more
complex algorithms
Kind: inner method of dft
Returns: Object - the resulted dft as an object { real, imag }
| Param | Type | Description |
| --- | --- | --- |
| src | Object | The source source buffers. The imaginary part can be null. |
| dest | Object | The destation bufers. The imaginary part can be null. |
| inverse | Boolean | Perform inverse DFT |
Kind: inner method of dft
Returns: Object - the complex signal (an objects with the form:
{ real: Array
| Param | Type | Description |
| --- | --- | --- |
| signal | Array | The (real) signal array |
Kind: inner method of dft
Returns: Array - the resulting real signal
| Param | Type | Description |
| --- | --- | --- |
| signal | Object | The complex signal as an object with two arrays |