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，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网站的支持！
