I have a list of sampled data from the WAV file. I would like to pass in these values into a library and get the frequency of the music played in the WAV file. For now, I will have 1 frequency in the WAV file and I would like to find a library that is compatible with Android. I understand that I need to use FFT to get the frequency domain. Is there any good libraries for that? I found that [KissFFT][1] is quite popular but I am not very sure how compatible it is on Android. Is there an easier and good library that can perform the task I want?

EDIT: I tried to use JTransforms to get the FFT of the WAV file but always failed at getting the correct frequency of the file. Currently, the WAV file contains sine curve of 440Hz, music note A4. However, I got the result as 441. Then I tried to get the frequency of G4, I got the result as 882Hz which is incorrect. The frequency of G4 is supposed to be 783Hz. Could it be due to not enough samples? If yes, how much samples should I take?

//DFT
DoubleFFT_1D fft = new DoubleFFT_1D(numOfFrames);
double max_fftval = -1;
int max_i = -1;
double[] fftData = new double[numOfFrames * 2];
for (int i = 0; i < numOfFrames; i++) {
    // copying audio data to the fft data buffer, imaginary part is 0
    fftData[2 * i] = buffer[i];
    fftData[2 * i + 1] = 0;
}
fft.complexForward(fftData);
for (int i = 0; i < fftData.length; i += 2) {
    // complex numbers -> vectors, so we compute the length of the vector, which is sqrt(realpart^2+imaginarypart^2)
        double vlen = Math.sqrt((fftData[i] * fftData[i]) + (fftData[i + 1] * fftData[i + 1]));
        //fd.append(Double.toString(vlen));
       // fd.append(",");
        if (max_fftval < vlen) {
            // if this length is bigger than our stored biggest length
            max_fftval = vlen;
            max_i = i;
        }
}
//double dominantFreq = ((double)max_i / fftData.length) * sampleRate;
double dominantFreq = (max_i/2.0) * sampleRate / numOfFrames;
fd.append(Double.toString(dominantFreq));

Can someone help me out?

EDIT2: I manage to fix the problem mentioned above by increasing the number of samples to 100000, however, sometimes I am getting the overtones as the frequency. Any idea how to fix it? Should I use Harmonic Product Frequency or Autocorrelation algorithms?