Fft Library in Android Sdk

Fast Fourier Transform in Android

check this out: FFT library in android Sdk

I personally am using JFFT pack which runs smoothly at analysing audio with an fft window size of 2048.

Add FFT algorithm to android studio

If your project has a libs folder for external dependencies then you are good. Else create one under the main project. If you have the jar with all dependencies for the FFT algorithm, put it in the above mentioned libs folder. Next open build.gradle under app and add the following line under dependencies if it not already there..:

compile fileTree(dir: 'libs', include: ['*.jar'])

Sync the project as you usually do.

Android mp3 analysis

I finally found the solution!
I'm using the Minim java audio library, which can performs offline analysis on the audio buffer :)
http://code.compartmental.net/tools/minim/manual-minim/

Using FFT in TarsosDSP library in android

Try this code:

    new AndroidFFMPEGLocator(this);
    new Thread(new Runnable() {
        @Override
        public void run() {
            File externalStorage = Environment.getExternalStorageDirectory();
            File sourceFile = new File(externalStorage.getAbsolutePath() , "/440.mp3");

            final int bufferSize = 4096;
            final int fftSize = bufferSize / 2;
            final int sampleRate = 44100;

            AudioDispatcher audioDispatcher;
            audioDispatcher = AudioDispatcherFactory.fromPipe(sourceFile.getAbsolutePath(), sampleRate, bufferSize, 0);
            audioDispatcher.addAudioProcessor(new AudioProcessor() {

                FFT fft = new FFT(bufferSize);
                final float[] amplitudes = new float[fftSize];

                @Override
                public boolean process(AudioEvent audioEvent) {
                    float[] audioBuffer = audioEvent.getFloatBuffer();
                    fft.forwardTransform(audioBuffer);
                    fft.modulus(audioBuffer, amplitudes);

                    for (int i = 0; i < amplitudes.length; i++) {
                        Log.d(TAG, String.format("Amplitude at %3d Hz: %8.3f", (int) fft.binToHz(i, sampleRate) , amplitudes[i]));
                    }

                    return true;
                }

                @Override
                public void processingFinished() {

                }
            });
            audioDispatcher.run();
        }
    }).start();

It based on TarsosDSP Manual (page 12) and calculates and shows in log FFT for each bufferSize of 440.mp3 file (test 440Hz tone) on External storage (SD card). You should add TarsosDSP-Android-2.3.jar (or newer) from here to libs folder of your project (and add it as a library) and corresponding to your device ffmpeg library (armeabi-v7a_ffmpeg, armeabi-v7a-neon_ffmpeg or x86_ffmpeg) from here to assets folder of your project.

And don't forget add

<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />

to AndroidManifest.xml (and also give runtime permissions for targetSdkVersion higher then 21)

PS. You can give FFT for whole file if bufferSize will be equals to number of samples in source file (or bigger).

Android Values are all 0 after FFT

Your inputs are all 0.

short s = 32767;
double d = s/32768;
System.out.println("dividing a short yields a truncated result " + d);
d = (double) s / 32768;
System.out.println("casting to a double and then dividing yields " + d);

FFT on EEG signal in Android understanding the code

An fft should return a series of complex numbers (could either be rectangular coordinates, or polar: phase and magnitude) for a specific range of frequencies...

I'm still working through the expressions, but I'll bet dollars to donuts that the x and y arrays are the real (x) and imaginary (y) components of the complex numbers that are the result of the transformation.

The absolute value of the sum of the squares of these two components should be the magnitude of the harmonic component at each frequency (conversion to polar).

If the phase is important for your application, keep in mind that the the FFT (as with any phasor) can either be sine referenced or cosine referenced. I beleive sine is the standard, however.

see:

http://www.mathworks.com/help/matlab/ref/fft.html

http://mathworld.wolfram.com/FastFourierTransform.html

Since the FFT gives a truncated approximation to an infinite series created by a harmonic decomposition of a periodic waveform any periodic waveform can be used to test the functionality of your code.

For an example, a square wave should be easy to replicate, and has very well known harmonic coefficients. The resolution of the data set will determine the number of harmonics that you can calculate (most fft algorithms do best with a data set that has a length equal to a power of two, and is a number of integral wavelengths of the longest frequency that you want to use).

The square wave coefficients should be at odd multiples of the fundamental frequency and have magnitudes that vary inversely with the order of the harmonic.

http://en.wikipedia.org/wiki/Square_wave

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