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* Init App * init server.py (#93) * init server.py * Update requirements.txt Add requirement Co-authored-by: auau <auau@test.com> Co-authored-by: babysor00 <babysor00@gmail.com> * Run web.py！ Run web.py！ Co-authored-by: balala <Ozgay@users.noreply.github.com> Co-authored-by: auau <auau@test.com>
112 lines
2.8 KiB
JavaScript
112 lines
2.8 KiB
JavaScript
/*
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时域转频域，快速傅里叶变换(FFT)
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https://github.com/xiangyuecn/Recorder
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var fft=Recorder.LibFFT(bufferSize)
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bufferSize取值2的n次方
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fft.bufferSize 实际采用的bufferSize
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fft.transform(inBuffer)
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inBuffer:[Int16,...] 数组长度必须是bufferSize
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返回[Float64(Long),...]，长度为bufferSize/2
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*/
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/*
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从FFT.java 移植，Java开源库：jmp123 版本0.3
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https://www.iteye.com/topic/851459
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https://sourceforge.net/projects/jmp123/files/
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*/
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Recorder.LibFFT=function(bufferSize){
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"use strict";
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var FFT_N_LOG,FFT_N,MINY;
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var real, imag, sintable, costable;
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var bitReverse;
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var FFT_Fn=function(bufferSize) {//bufferSize只能取值2的n次方
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FFT_N_LOG=Math.round(Math.log(bufferSize)/Math.log(2));
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FFT_N = 1 << FFT_N_LOG;
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MINY = ((FFT_N << 2) * Math.sqrt(2));
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real = [];
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imag = [];
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sintable = [0];
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costable = [0];
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bitReverse = [];
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var i, j, k, reve;
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for (i = 0; i < FFT_N; i++) {
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k = i;
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for (j = 0, reve = 0; j != FFT_N_LOG; j++) {
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reve <<= 1;
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reve |= (k & 1);
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k >>>= 1;
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}
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bitReverse[i] = reve;
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}
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var theta, dt = 2 * Math.PI / FFT_N;
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for (i = (FFT_N >> 1) - 1; i > 0; i--) {
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theta = i * dt;
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costable[i] = Math.cos(theta);
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sintable[i] = Math.sin(theta);
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}
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}
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/*
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用于频谱显示的快速傅里叶变换
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inBuffer 输入FFT_N个实数，返回 FFT_N/2个输出值(复数模的平方)。
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*/
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var getModulus=function(inBuffer) {
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var i, j, k, ir, j0 = 1, idx = FFT_N_LOG - 1;
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var cosv, sinv, tmpr, tmpi;
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for (i = 0; i != FFT_N; i++) {
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real[i] = inBuffer[bitReverse[i]];
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imag[i] = 0;
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}
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for (i = FFT_N_LOG; i != 0; i--) {
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for (j = 0; j != j0; j++) {
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cosv = costable[j << idx];
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sinv = sintable[j << idx];
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for (k = j; k < FFT_N; k += j0 << 1) {
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ir = k + j0;
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tmpr = cosv * real[ir] - sinv * imag[ir];
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tmpi = cosv * imag[ir] + sinv * real[ir];
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real[ir] = real[k] - tmpr;
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imag[ir] = imag[k] - tmpi;
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real[k] += tmpr;
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imag[k] += tmpi;
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}
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}
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j0 <<= 1;
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idx--;
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}
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j = FFT_N >> 1;
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var outBuffer=new Float64Array(j);
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/*
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* 输出模的平方:
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* for(i = 1; i <= j; i++)
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* inBuffer[i-1] = real[i] * real[i] + imag[i] * imag[i];
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*
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* 如果FFT只用于频谱显示,可以"淘汰"幅值较小的而减少浮点乘法运算. MINY的值
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* 和Spectrum.Y0,Spectrum.logY0对应.
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*/
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sinv = MINY;
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cosv = -MINY;
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for (i = j; i != 0; i--) {
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tmpr = real[i];
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tmpi = imag[i];
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if (tmpr > cosv && tmpr < sinv && tmpi > cosv && tmpi < sinv)
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outBuffer[i - 1] = 0;
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else
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outBuffer[i - 1] = Math.round(tmpr * tmpr + tmpi * tmpi);
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}
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return outBuffer;
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}
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FFT_Fn(bufferSize);
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return {transform:getModulus,bufferSize:FFT_N};
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};
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