Java实现Shazam声音识别算法的实例代码

shazam算法采用傅里叶变换将时域信号转换为频域信号，并获得音频指纹，最后匹配指纹契合度来识别音频。

1、audiosystem获取音频

奈奎斯特-香农采样定理告诉我们，为了能捕获人类能听到的声音频率，我们的采样速率必须是人类听觉范围的两倍。人类能听到的声音频率范围大约在20hz到20000hz之间，所以在录制音频的时候采样率大多是44100hz。这是大多数标准mpeg-1 的采样率。44100这个值最初来源于索尼，因为它可以允许音频在修改过的视频设备上以25帧（pal）或者30帧（ ntsc）每秒进行录制，而且也覆盖了专业录音设备的20000hz带宽。所以当你在选择录音的频率时，选择44100hz就好了。

定义音频格式：

  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);
  }

调用麦克风获取音频，保存到out中

 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();
    }

获取到的out数据需要通过傅里叶变换，从时域信号转换为频域信号。

傅里叶变换

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;
  }

计算out的频域值

 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());
  }

总结

以上所述是小编给大家介绍的java实现shazam声音识别算法的实例代码，希望对大家有所帮助