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：
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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);
}
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调用麦克风获取音频，保存到out中
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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();
}
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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
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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;
}
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计算out的频域值
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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());
}
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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，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对服务器之家网站的支持！
原文链接：https://blog.csdn.net/llhhzz1989/article/details/82585957