java 实现声音识别算法 Java实现Shazam声音识别算法的实例代码

  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;
    // 因为exp(-2i*n*PI)=1，n=1时递归原点
    if (n == 1){
      return x;
    }
    // 如果信号数为奇数，使用dft计算
    if (n % 2 != 0) {
      return dft(x);
    }
    // 提取下标为偶数的原始信号值进行递归fft计算
    Complex[] even = new Complex[n / 2];
    for (int k = 0; k < n / 2; k++) {
      even[k] = x[2 * k];
    }
    Complex[] evenValue = fft(even);
    // 提取下标为奇数的原始信号值进行fft计算
    // 节约内存
    Complex[] odd = even;
    for (int k = 0; k < n / 2; k++) {
      odd[k] = x[2 * k + 1];
    }
    Complex[] oddValue = fft(odd);
    // 偶数+奇数
    Complex[] result = new Complex[n];
    for (int k = 0; k < n / 2; k++) {
      // 使用欧拉公式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) 相当于 -exp(-2*k*PI/n)，其中exp(-n*PI)=-1(欧拉公式);
      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());
  }