首先，我对Android很新。在此之前我正在做Arduino。我想实现的第一个目标是能够检测特定的声音频率或模式。
因此，在我甚至可以检测到特定的声音频率之前，我必须能够至少看到所有频率。
在没有尝试的情况下，任何人都可以将此标记为重复或问题。我尽我所能，尽力而为。我几乎无法理解FFT如何工作或零交叉工作。但我已经读到过零交叉并不那么准确。所以，我正在使用FFT。我在这里非常密切地关注这篇文章，但我不知道整个工作代码是怎么样的。
Android audio FFT to retrieve specific frequency magnitude using audiorecord
所以，我尝试将问题和解决方案结合起来，如下面的代码所示。我几乎无法理解发生了什么。
FrequencyThread.java
package com.example.syahmul.backgroundapp;
/**
* Record and detect frequency using FFT
* Ref: https://stackoverflow.com/questions/5774104/android-audio-fft-to-retrieve-specific-frequency-
* magnitude-using-audiorecord
*/
//Audio Library
import android.media.AudioRecord;
import android.media.MediaRecorder.AudioSource;
import android.media.AudioFormat;
import android.provider.MediaStore;
import android.util.Log;
public class FrequencyThread extends Thread{
protected static final String TAG = "FrequencyThread";
int channel_config = AudioFormat.CHANNEL_IN_MONO;
int format = AudioFormat.ENCODING_PCM_16BIT;
public int sampleSize = 8000;
//public int bufferSize = AudioRecord.getMinBufferSize(sampleSize, channel_config, format); //640
public int bufferSize = 1024; //hardcode this so that can get power of 2
AudioRecord audioInput = new AudioRecord(AudioSource.MIC, sampleSize, channel_config, format, bufferSize);
private double ComputeFrequency(int arrayIndex) {
//return ((1.0 * sampleRate) / (1.0 * fftOutWindowSize)) * arrayIndex;
return ((1.0 * sampleSize) / (1.0 * bufferSize)) * arrayIndex;
}
@Override
public void run() {
Log.i(TAG, "bufferSize: " + String.valueOf(bufferSize));
//Read audio
byte[] audioBuffer = new byte[bufferSize]; //short
audioInput.startRecording();
audioInput.read(audioBuffer, 0, bufferSize);
int bytesRecorded = audioBuffer.length;
Log.i(TAG, "bytesRecorded: " + String.valueOf(bytesRecorded));
double[] micBufferData = new double[bufferSize];
final int bytesPerSample = 2; // As it is 16bit PCM
final double amplification = 100.0; // choose a number as you like
for (int index = 0, floatIndex = 0; index < bytesRecorded - bytesPerSample + 1; index += bytesPerSample, floatIndex++) {
double sample = 0;
for (int b = 0; b < bytesPerSample; b++) {
//int v = bufferData[index + b];
int v = audioBuffer[index + b]; //what the hell is bufferData??
if (b < bytesPerSample - 1 || bytesPerSample == 1) {
v &= 0xFF;
}
sample += v << (b * 8);
}
double sample32 = amplification * (sample / 32768.0);
micBufferData[floatIndex] = sample32;
//Log.i(TAG, "micBufferData[" + floatIndex + "]: " + String.valueOf(micBufferData[floatIndex]));
}
Log.i(TAG, "Before feeing complex array with micbufferdata");
//Feed complex Array with micBufferData
Complex[] fftTempArray = new Complex[bufferSize];
for (int i=0; i<bufferSize; i++) {
fftTempArray[i] = new Complex(micBufferData[i], 0); //audio - micBufferData is empty!!
Log.i(TAG, "fftTempArray[" + i + "]: " + String.valueOf(fftTempArray[i]));
}
Log.i(TAG, "fftTempArray length: " + String.valueOf(fftTempArray.length));
//Complex[] fftArray = fft(fftTempArray);
Complex[] fftArray = FFT.fft(fftTempArray); //this one is the cause of the crash. N is not
//a power of 2
for (int a=0; a<fftArray.length; a++) {
//The frequency below make no sense!! Why is it sequential?
Log.i(TAG, "computerFrequency: " + String.valueOf(ComputeFrequency(a)));
}
}
}
FFT.java
/*************************************************************************
* Compilation: javac FFT.java
* Execution: java FFT N
* Dependencies: Complex.java
*
* Compute the FFT and inverse FFT of a length N complex sequence.
* Bare bones implementation that runs in O(N log N) time. Our goal
* is to optimize the clarity of the code, rather than performance.
*
* Limitations
* -----------
* - assumes N is a power of 2
*
* - not the most memory efficient algorithm (because it uses
* an object type for representing complex numbers and because
* it re-allocates memory for the subarray, instead of doing
* in-place or reusing a single temporary array)
*
*************************************************************************/
package com.example.syahmul.backgroundapp;
import android.util.Log;
import static android.util.Log.i;
public class FFT {
protected static final String TAG = "FFT";
// compute the FFT of x[], assuming its length is a power of 2
public static Complex[] fft(Complex[] x) {
int N = x.length;
//int N = 1024; //I add this myself. To reflect power of 2. It did'nt work either
Log.i(TAG, "N: " + String.valueOf(N));
// base case
if (N == 1) {
Log.i(TAG, "N == 1 ");
return new Complex[]{x[0]};
}
// radix 2 Cooley-Tukey FFT
if (N % 2 != 0) {
Log.i(TAG, "N%2 != 0"); //It only executed once
throw new RuntimeException("N is not a power of 2");
}
// fft of even terms
Complex[] even = new Complex[N/2];
Log.i(TAG, "even length: " + even.length); //It only executed once
for (int k = 0; k < N/2; k++) {
even[k] = x[2*k];
}
Complex[] q = fft(even);
// fft of odd terms
Complex[] odd = even; // reuse the array
for (int k = 0; k < N/2; k++) {
odd[k] = x[2*k + 1];
}
Complex[] r = fft(odd);
// combine
Complex[] y = new Complex[N];
for (int k = 0; k < N/2; k++) {
double kth = -2 * k * Math.PI / N;
Complex wk = new Complex(Math.cos(kth), Math.sin(kth));
y[k] = q[k].plus(wk.times(r[k]));
y[k + N/2] = q[k].minus(wk.times(r[k]));
}
return y;
}
// compute the inverse FFT of x[], assuming its length is a power of 2
public static Complex[] ifft(Complex[] x) {
int N = x.length;
Complex[] y = new Complex[N];
// take conjugate
for (int i = 0; i < N; i++) {
y[i] = x[i].conjugate();
}
// compute forward FFT
y = fft(y);
// take conjugate again
for (int i = 0; i < N; i++) {
y[i] = y[i].conjugate();
}
// divide by N
for (int i = 0; i < N; i++) {
y[i] = y[i].times(1.0 / N);
}
return y;
}
// compute the circular convolution of x and y
public static Complex[] cconvolve(Complex[] x, Complex[] y) {
// should probably pad x and y with 0s so that they have same length
// and are powers of 2
if (x.length != y.length) { throw new RuntimeException("Dimensions don't agree"); }
int N = x.length;
// compute FFT of each sequence
Complex[] a = fft(x);
Complex[] b = fft(y);
// point-wise multiply
Complex[] c = new Complex[N];
for (int i = 0; i < N; i++) {
c[i] = a[i].times(b[i]);
}
// compute inverse FFT
return ifft(c);
}
// compute the linear convolution of x and y
public static Complex[] convolve(Complex[] x, Complex[] y) {
Complex ZERO = new Complex(0, 0);
Complex[] a = new Complex[2*x.length];
for (int i = 0; i < x.length; i++) a[i] = x[i];
for (int i = x.length; i < 2*x.length; i++) a[i] = ZERO;
Complex[] b = new Complex[2*y.length];
for (int i = 0; i < y.length; i++) b[i] = y[i];
for (int i = y.length; i < 2*y.length; i++) b[i] = ZERO;
return cconvolve(a, b);
}
// display an array of Complex numbers to standard output
public static void show(Complex[] x, String title) {
System.out.println(title);
System.out.println("-------------------");
for (int i = 0; i < x.length; i++) {
System.out.println(x[i]);
}
System.out.println();
}
/*********************************************************************
* Test client and sample execution
*
* % java FFT 4
* x
* -------------------
* -0.03480425839330703
* 0.07910192950176387
* 0.7233322451735928
* 0.1659819820667019
*
* y = fft(x)
* -------------------
* 0.9336118983487516
* -0.7581365035668999 + 0.08688005256493803i
* 0.44344407521182005
* -0.7581365035668999 - 0.08688005256493803i
*
* z = ifft(y)
* -------------------
* -0.03480425839330703
* 0.07910192950176387 + 2.6599344570851287E-18i
* 0.7233322451735928
* 0.1659819820667019 - 2.6599344570851287E-18i
*
* c = cconvolve(x, x)
* -------------------
* 0.5506798633981853
* 0.23461407150576394 - 4.033186818023279E-18i
* -0.016542951108772352
* 0.10288019294318276 + 4.033186818023279E-18i
*
* d = convolve(x, x)
* -------------------
* 0.001211336402308083 - 3.122502256758253E-17i
* -0.005506167987577068 - 5.058885073636224E-17i
* -0.044092969479563274 + 2.1934338938072244E-18i
* 0.10288019294318276 - 3.6147323062478115E-17i
* 0.5494685269958772 + 3.122502256758253E-17i
* 0.240120239493341 + 4.655566391833896E-17i
* 0.02755001837079092 - 2.1934338938072244E-18i
* 4.01805098805014E-17i
*
*********************************************************************/
public static void main(String[] args) {
int N = Integer.parseInt(args[0]);
Complex[] x = new Complex[N];
// original data
for (int i = 0; i < N; i++) {
x[i] = new Complex(i, 0);
x[i] = new Complex(-2*Math.random() + 1, 0);
}
show(x, "x");
// FFT of original data
Complex[] y = fft(x);
show(y, "y = fft(x)");
// take inverse FFT
Complex[] z = ifft(y);
show(z, "z = ifft(y)");
// circular convolution of x with itself
Complex[] c = cconvolve(x, x);
show(c, "c = cconvolve(x, x)");
// linear convolution of x with itself
Complex[] d = convolve(x, x);
show(d, "d = convolve(x, x)");
}
}
Complex.java
/*************************************************************************
* Compilation: javac Complex.java
* Execution: java Complex
*
* Data type for complex numbers.
*
* The data type is "immutable" so once you create and initialize
* a Complex object, you cannot change it. The "final" keyword
* when declaring re and im enforces this rule, making it a
* compile-time error to change the .re or .im fields after
* they've been initialized.
*
* % java Complex
* a = 5.0 + 6.0i
* b = -3.0 + 4.0i
* Re(a) = 5.0
* Im(a) = 6.0
* b + a = 2.0 + 10.0i
* a - b = 8.0 + 2.0i
* a * b = -39.0 + 2.0i
* b * a = -39.0 + 2.0i
* a / b = 0.36 - 1.52i
* (a / b) * b = 5.0 + 6.0i
* conj(a) = 5.0 - 6.0i
* |a| = 7.810249675906654
* tan(a) = -6.685231390246571E-6 + 1.0000103108981198i
*
*************************************************************************/
package com.example.syahmul.backgroundapp;
public class Complex {
private final double re; // the real part
private final double im; // the imaginary part
// create a new object with the given real and imaginary parts
public Complex(double real, double imag) {
re = real;
im = imag;
}
// return a string representation of the invoking Complex object
public String toString() {
if (im == 0) return re + "";
if (re == 0) return im + "i";
if (im < 0) return re + " - " + (-im) + "i";
return re + " + " + im + "i";
}
// return abs/modulus/magnitude and angle/phase/argument
public double abs() { return Math.hypot(re, im); } // Math.sqrt(re*re + im*im)
public double phase() { return Math.atan2(im, re); } // between -pi and pi
// return a new Complex object whose value is (this + b)
public Complex plus(Complex b) {
Complex a = this; // invoking object
double real = a.re + b.re;
double imag = a.im + b.im;
return new Complex(real, imag);
}
// return a new Complex object whose value is (this - b)
public Complex minus(Complex b) {
Complex a = this;
double real = a.re - b.re;
double imag = a.im - b.im;
return new Complex(real, imag);
}
// return a new Complex object whose value is (this * b)
public Complex times(Complex b) {
Complex a = this;
double real = a.re * b.re - a.im * b.im;
double imag = a.re * b.im + a.im * b.re;
return new Complex(real, imag);
}
// scalar multiplication
// return a new object whose value is (this * alpha)
public Complex times(double alpha) {
return new Complex(alpha * re, alpha * im);
}
// return a new Complex object whose value is the conjugate of this
public Complex conjugate() { return new Complex(re, -im); }
// return a new Complex object whose value is the reciprocal of this
public Complex reciprocal() {
double scale = re*re + im*im;
return new Complex(re / scale, -im / scale);
}
// return the real or imaginary part
public double re() { return re; }
public double im() { return im; }
// return a / b
public Complex divides(Complex b) {
Complex a = this;
return a.times(b.reciprocal());
}
// return a new Complex object whose value is the complex exponential of this
public Complex exp() {
return new Complex(Math.exp(re) * Math.cos(im), Math.exp(re) * Math.sin(im));
}
// return a new Complex object whose value is the complex sine of this
public Complex sin() {
return new Complex(Math.sin(re) * Math.cosh(im), Math.cos(re) * Math.sinh(im));
}
// return a new Complex object whose value is the complex cosine of this
public Complex cos() {
return new Complex(Math.cos(re) * Math.cosh(im), -Math.sin(re) * Math.sinh(im));
}
// return a new Complex object whose value is the complex tangent of this
public Complex tan() {
return sin().divides(cos());
}
// a static version of plus
public static Complex plus(Complex a, Complex b) {
double real = a.re + b.re;
double imag = a.im + b.im;
Complex sum = new Complex(real, imag);
return sum;
}
// sample client for testing
public static void main(String[] args) {
Complex a = new Complex(5.0, 6.0);
Complex b = new Complex(-3.0, 4.0);
System.out.println("a = " + a);
System.out.println("b = " + b);
System.out.println("Re(a) = " + a.re());
System.out.println("Im(a) = " + a.im());
System.out.println("b + a = " + b.plus(a));
System.out.println("a - b = " + a.minus(b));
System.out.println("a * b = " + a.times(b));
System.out.println("b * a = " + b.times(a));
System.out.println("a / b = " + a.divides(b));
System.out.println("(a / b) * b = " + a.divides(b).times(b));
System.out.println("conj(a) = " + a.conjugate());
System.out.println("|a| = " + a.abs());
System.out.println("tan(a) = " + a.tan());
}
}
computeFrequency输出为0.0 - 7992.1875，依次增量为7.8125
0.0
7.8125
15.625
23.4375
31.25
39.0625
.
.
7992.1875
我硬编码了下面因为我不断得到＆＃34; N不是2的力量＆＃34;：
//public int bufferSize = AudioRecord.getMinBufferSize(sampleSize, channel_config, format); //640
public int bufferSize = 1024; //hardcode this so that can get power of 2
确定。这完全是关于它的。我希望我在这里很清楚。请告诉我如何才能获得正确的频率。
此外，超出了这个问题的范围，我怎样才能在这样的情况下改变它，不断检测特定的频率。 ：）