Minim 

FFT Fields
BARTLETT Methods
avgSize ( ) 
FFT stands for Fast Fourier Transform. It is an efficient way to calculate the Complex
Discrete Fourier Transform. There is not much to say about this class other than the fact
that when you want to analyze the spectrum of an audio buffer you will almost always use
this class. One restriction of this class is that the audio buffers you want to analyze
must have a length that is a power of two. If you try to construct an FFT with a
timeSize that is not a power of two, an IllegalArgumentException will be
thrown.
A Fourier Transform is an algorithm that transforms a signal in the time
domain, such as a sample buffer, into a signal in the frequency domain, often
called the spectrum. The spectrum does not represent individual frequencies,
but actually represents frequency bands centered on particular frequencies.
The center frequency of each band is usually expressed as a fraction of the
sampling rate of the time domain signal and is equal to the index of the
frequency band divided by the total number of bands. The total number of
frequency bands is usually equal to the length of the time domain signal, but
access is only provided to frequency bands with indices less than half the
length, because they correspond to frequencies below the Nyquist frequency.
In other words, given a signal of length
As an example, if you construct an FFT with a
Usage
A typical usage of the FFT is to analyze a signal so that the
frequency spectrum may be represented in some way, typically with vertical
lines. You could do this in Processing with the following code, where
fft.forward(audio.left); for (int i = 0; i < fft.specSize(); i++) { // draw the line for frequency band i, scaling it by 4 so we can see it a bit better line(i, height, i, height  fft.getBand(i) * 4); }Windowing Windowing is the process of shaping the audio samples before transforming them to the frequency domain. The Fourier Transform assumes the sample buffer is is a repetitive signal, if a sample buffer is not truly periodic within the measured interval sharp discontinuities may arise that can introduce spectral leakage. Spectral leakage is the speading of signal energy across multiple FFT bins. This "spreading" can drown out narrow band signals and hinder detection.
A windowing function
attempts to reduce spectral leakage by attenuating the measured sample buffer
at its end points to eliminate discontinuities. If you call the Averages FFT also has functions that allow you to request the creation of an average spectrum. An average spectrum is simply a spectrum with fewer bands than the full spectrum where each average band is the average of the amplitudes of some number of contiguous frequency bands in the full spectrum.
If you don't want any averages calculated, then you can call
Inverse Transform
FFT also supports taking the inverse transform of a spectrum.
This means that a frequency spectrum will be transformed into a time domain
signal and placed in a provided sample buffer. The length of the time domain
signal will be Constructors Constructs an FFT that will accept sample buffers that are Parameters timeSize — int: the length of the sample buffers you will be analyzingsampleRate — float: the sample rate of the audio you will be analyzing Related Example /** * This sketch demonstrates how to use an FFT to analyze * the audio being generated by an AudioPlayer. * <p> * FFT stands for Fast Fourier Transform, which is a * method of analyzing audio that allows you to visualize * the frequency content of a signal. You've seen * visualizations like this before in music players * and car stereos. * <p> * For more information about Minim and additional features, * visit http://code.compartmental.net/minim/ */ import ddf.minim.analysis.*; import ddf.minim.*; Minim minim; AudioPlayer jingle; FFT fft; void setup() { size(512, 200, P3D); minim = new Minim(this); // specify that we want the audio buffers of the AudioPlayer // to be 1024 samples long because our FFT needs to have // a poweroftwo buffer size and this is a good size. jingle = minim.loadFile("jingle.mp3", 1024); // loop the file indefinitely jingle.loop(); // create an FFT object that has a timedomain buffer // the same size as jingle's sample buffer // note that this needs to be a power of two // and that it means the size of the spectrum will be half as large. fft = new FFT( jingle.bufferSize(), jingle.sampleRate() ); } void draw() { background(0); stroke(255); // perform a forward FFT on the samples in jingle's mix buffer, // which contains the mix of both the left and right channels of the file fft.forward( jingle.mix ); for(int i = 0; i < fft.specSize(); i++) { // draw the line for frequency band i, scaling it up a bit so we can see it line( i, height, i, height  fft.getBand(i)*8 ); } } Usage Web & Application 