public static float sampleRate = 44100;  public static int sampleSizeInBits = 16;  public static int channels = 2; // double  public static boolean signed = true; // Indicates whether the data is signed or unsigned  public static boolean bigEndian = true; // Indicates whether the audio data is stored in big-endian or little-endian order  public AudioFormat getFormat() {    return new AudioFormat(sampleRate, sampleSizeInBits, channels, signed,        bigEndian);  }

 public static ByteArrayOutputStream out = new ByteArrayOutputStream();1    try {      AudioFormat format = smartAuto.getFormat(); // Fill AudioFormat with the settings      DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);      startTime = new Date().getTime();      System.out.println(startTime);      SmartAuto.line = (TargetDataLine) AudioSystem.getLine(info);      SmartAuto.line.open(format);      SmartAuto.line.start();      new FileAnalysis().getDataToOut("");      while (smartAuto.running) {        checkTime(startTime);      }      SmartAuto.line.stop();      SmartAuto.line.close();    } catch (Throwable e) {      e.printStackTrace();    }

public Complex[] fft(Complex[] x) {    int n = x.length;    // 因為exp(-2i*n*PI)=1，n=1時遞歸原點    if (n == 1){      return x;    }    // 如果信號數為奇數，使用dft計算    if (n % 2 != 0) {      return dft(x);    }    // 提取下標為偶數的原始信號值進行遞歸fft計算    Complex[] even = new Complex[n / 2];    for (int k = 0; k < n / 2; k++) {      even[k] = x[2 * k];    }    Complex[] evenValue = fft(even);    // 提取下標為奇數的原始信號值進行fft計算    // 節約內存    Complex[] odd = even;    for (int k = 0; k < n / 2; k++) {      odd[k] = x[2 * k + 1];    }    Complex[] oddValue = fft(odd);    // 偶數+奇數    Complex[] result = new Complex[n];    for (int k = 0; k < n / 2; k++) {      // 使用歐拉公式e^(-i*2pi*k/N) = cos(-2pi*k/N) + i*sin(-2pi*k/N)      double p = -2 * k * Math.PI / n;      Complex m = new Complex(Math.cos(p), Math.sin(p));      result[k] = evenValue[k].add(m.multiply(oddValue[k]));      // exp(-2*(k+n/2)*PI/n) 相當于 -exp(-2*k*PI/n)，其中exp(-n*PI)=-1(歐拉公式);      result[k + n / 2] = evenValue[k].subtract(m.multiply(oddValue[k]));    }    return result;  }

 private void setFFTResult(){    byte audio[] = SmartAuto.out.toByteArray();    final int totalSize = audio.length;    System.out.println("totalSize = " + totalSize);    int chenkSize = 4;    int amountPossible = totalSize/chenkSize;    //When turning into frequency domain we'll need complex numbers:     SmartAuto.results = new Complex[amountPossible][];    DftOperate dfaOperate = new DftOperate();    //For all the chunks:     for(int times = 0;times < amountPossible; times++) {      Complex[] complex = new Complex[chenkSize];      for(int i = 0;i < chenkSize;i++) {        //Put the time domain data into a complex number with imaginary part as 0:         complex[i] = new Complex(audio[(times*chenkSize)+i], 0);      }      //Perform FFT analysis on the chunk:       SmartAuto.results[times] = dfaOperate.fft(complex);    }    System.out.println("results = " + SmartAuto.results.toString());  }