/jdsp

Digital Signal Processing Utilities for Jai

Primary LanguageC

jdsp - Jai Digital Signal Processing Utilities

jfft

Feature Description
jfft Fast Fourier Transforms with various windowing functions
jdsp_filter WIP Various filters including lowpass, highpass, bandstop, and bandpass

jfft

jfft only processes real data sets at this time. No complex numbers. This means that instead of real/imaginary components we have sine/cosine.

jfft_create_transformer :: (signal_length: int, allocator: Allocator = temp) -> *jfft_transformer #must

Sets up the memory needed to perform FFTs on inputs of a specific length. signal_length should be a power of two for best performance, but the only real requirement is that signal_length is even. Internal memory can be reused by jfft_forward and jfft_backward.

jfft_destroy_transformer :: (jfft: *jfft_transformer)

If you used something other than temp as an allocator or you don't call reset_temporary_storage too often you can call this.

jfft_forward :: (jfft: *jfft_transformer, input: []float32)

Performs an FFT on a given input buffer and puts sine/cosine components . input should match the signal_length of the transformer. DC is in bin 0. Bins 1..N/2-1 contain frequency bins. Nyquist starts at bin N/2. Results can be fed into jfft_magnitude_transform or jfft_magnitude_dB_transform for useful analytics.

jfft_forward_transform :: (input: []float32, allocator: Allocator = temp)

Creates *jfft_transformer internally. Same as jfft_forward otherwise. Good as a one-off.

jfft_backward :: (jfft: *jfft_transformer, input: []float32)

Performs an inverse FFT on data returned from jfft_forward. Floating point errors mean that the original signal may not be able to be recreated exactly depending on original signal.

jfft_backward_transform :: (input: []float32, allocator: Allocator = temp)

Creates *jfft_transformer internally. Same as jfft_backward otherwise. Good as a one-off.

jfft_magnitude_transform :: (input: []float32)

Modifies input in-place. Converts results of jfft_forward to be magnitude data up to N/2 due to Nyquist-Shannon sampling theorem. This means that an FFT can only provide useful info for frequency bins of sample_rate / 2. Anything beyond N/2 is imaginary or something (useful for reconstruction).

jfft_magnitude :: (input: []float32, allocator: Allocator = temp) -> []float32 #must

Same as jfft_magnitude_transform but it returns a new array without touching original data.

jfft_magnitude_db_transform :: (input: []float32)

Similar to jfft_magnitude_transform except converts the first N/2 frequency bins to decibels.

jfft_magnitude_db :: (input: []float32, allocator: Allocator = temp) -> []float32 #must

Same as jfft_magnitude_db_transform but returns a new array without touching original data.

Modifications to the frequency-domain data from jfft_forward would require overlap-add for successful jfft_backward. The successful execution of Fourier synthesis from modified frequency-domain data is on the programmer and outside the scope of the API at this time.

jdsp_filter

Working on it!

Example

Go into example and run jai example.jai -release. You should have an sdl2 library accessible somewhere in case the one in modules doesn't work for you for whatever reason.

Run it like ./example.

UP/DOWN will increase/decrease your chunk for FFT.

SPACE will pause the graphs at the current chunk.

LEFT/RIGHT will move to next/previous frame when paused.

K/L will decrease/increase the time betweeen chunks when unpaused.

Click the Window combo box at the top to see how different windows affect your input signal and FFT results.