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Code Issues 17 Pull Requests Releases 2 Wiki Activity

ref #6 - autocorrelation implemented

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This commit is contained in:
toni
2017-11-27 21:17:30 +01:00
parent d0f0d0aa0b
commit 12c5cae253
5 changed files with 120 additions and 40 deletions
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4
android/ConductorsWatch/app/src/main/java/de/tonifetzer/conductorswatch/BpmEstimator.java
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@@ -6,7 +6,9 @@ import android.hardware.SensorEvent;
import android.hardware.SensorEventListener;
import android.hardware.SensorManager;
import android.os.Handler;
import android.util.Log;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.CopyOnWriteArrayList;
import java.util.concurrent.ThreadLocalRandom;
@@ -17,8 +19,6 @@ import de.tonifetzer.conductorswatch.utilities.Utils;
* Created by toni on 13/11/17.
*/
//TODO: diesen estimator testen. kommen alle messungen? wie sind die messungen zeitlich voneinander verschieden?
//TODO: klapp das wirklich mit den 4ms. passt der buffer? gehen auch höhere zeiten?
//TODO: einfügen der logik autoCorr + FindPeaks
public class BpmEstimator implements SensorEventListener {

6
android/ConductorsWatch/app/src/main/java/de/tonifetzer/conductorswatch/WorkerFragment.java
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@@ -111,12 +111,6 @@ public class WorkerFragment extends Fragment implements Metronome.OnMetronomeLis
// stop the worker thread for bpm estimator
mBpmEstimator.stop();
/*mBpmThread.interrupt();
try {
mBpmThread.join();
} catch (InterruptedException e) {
e.printStackTrace();
}*/
// stop the worker thread for metronom
mMetronome.stop();

142
android/ConductorsWatch/app/src/main/java/de/tonifetzer/conductorswatch/utilities/Utils.java
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@@ -3,11 +3,9 @@ package de.tonifetzer.conductorswatch.utilities;
import android.content.Context;
import android.content.res.Resources;
import android.util.DisplayMetrics;
import org.jtransforms.fft.DoubleFFT_1D;
import org.jtransforms.fft.FloatFFT_1D;
import java.util.ArrayList;
import java.util.Queue;
import java.util.Arrays;
public class Utils {
@@ -40,18 +38,25 @@ public class Utils {
}
}
//TODO: implement methods providing x,y,z and ts as solo vectors
//TODO: implement sliding window counter
public static class AccelerometerWindowBuffer extends ArrayList<AccelerometerData> {
private int mWindowSize;
private int mOverlapSize;
private int mOverlapCounter;
private float[] mX;
private float[] mY;
private float[] mZ;
private long[] mTs;
public AccelerometerWindowBuffer(int windowSize, int overlap){
this.mWindowSize = windowSize;
this.mOverlapSize = overlap;
mOverlapCounter = 1;
mX = new float[this.mWindowSize];
mY = new float[this.mWindowSize];
mZ = new float[this.mWindowSize];
mTs = new long[this.mWindowSize];
}
public boolean add(AccelerometerData ad){
@@ -60,6 +65,14 @@ public class Utils {
removeRange(0, size() - mWindowSize);
}
//update the double arrays.
for (int i = 0; i < size(); ++i) {
mX[i] = get(i).x;
mY[i] = get(i).y;
mZ[i] = get(i).z;
mTs[i] = get(i).ts;
}
++mOverlapCounter;
return r;
}
@@ -79,34 +92,107 @@ public class Utils {
public AccelerometerData getOldest() {
return get(0);
}
public float[] getX(){
return mX;
}
public float[] getY(){
return mY;
}
public float[] getZ(){
return mZ;
}
public long[] getTs(){
return mTs;
}
}
public static double sqr(double x) {
public static float sqr(float x) {
return x * x;
}
//TODO: implement maxLag as input
//TODO: implement positive and negative lag output
public void fftAutoCorrelation(double [] x, double [] ac) {
int n = x.length;
// Assumes n is even.
DoubleFFT_1D fft = new DoubleFFT_1D(n);
fft.realForward(x);
//ac[0] = sqr(x[0]); // For normal xcov
ac[0] = 0; // For statistical convention, zero out the mean
ac[1] = sqr(x[1]);
for (int i = 2; i < n; i += 2) {
ac[i] = sqr(x[i]) + sqr(x[i+1]);
ac[i+1] = 0;
}
DoubleFFT_1D ifft = new DoubleFFT_1D(n);
ifft.realInverse(ac, true);
//For statistical convention, normalize by dividing through with variance
for (int i = 1; i < n; i++){
ac[i] /= ac[0];
}
ac[0] = 1;
public static int nextPow2(int a){
return a == 0 ? 0 : 32 - Integer.numberOfLeadingZeros(a - 1);
}
public static float mean(float[] data){
float sum = 0;
for (int i = 0; i < data.length; i++) {
sum += data[i];
}
return sum / data.length;
}
public static float[] removeZero(float[] array){
int j = 0;
for( int i=0; i<array.length; i++ )
{
if (array[i] != 0)
array[j++] = array[i];
}
float[] newArray = new float[j];
System.arraycopy( array, 0, newArray, 0, j );
return newArray;
}
//TODO: errorhandling maxLag = 0;
//TODO: größeren Testcase schreiben
public static float[] fftAutoCorrelation(float[] data, int maxLag) {
int n = data.length;
float[] x = Arrays.copyOf(data, n);
int mxl = Math.min(maxLag, n - 1);
int ceilLog2 = nextPow2(2*n -1);
int n2 = (int) Math.pow(2,ceilLog2);
// x - mean(x) (pointwise)
float x_mean = mean(x);
for(int i = 0; i < x.length; ++i){
x[i] -= x_mean;
}
// double the size of x and fill up with zeros. if x is not even, add additional 0
float[] x2 = new float[n2 * 2]; //need double the size for fft.realForwardFull (look into method description)
Arrays.fill(x2, 0);
System.arraycopy(x,0, x2, 0, x.length);
// x_fft calculate fft 1D
FloatFFT_1D fft = new FloatFFT_1D(n2);
fft.realForwardFull(x2);
// Cr = abs(x_fft).^2 (absolute with complex numbers is (r^2) + (i^2)
float[] Cr = new float[n2 * 2];
int j = 0;
for(int i = 0; i < x2.length; ++i){
Cr[j++] = sqr(x2[i]) + sqr(x2[i+1]);
++i; //skip the complex part
}
// ifft(Cr,[],1)
FloatFFT_1D ifft = new FloatFFT_1D(n2);
ifft.realInverseFull(Cr, true);
// remove complex part and scale/normalize
float[] c1 = new float[n2];
j = 0;
for(int i = 0; i < Cr.length; ++i){
c1[j++] = Cr[i] / Cr[0];
++i; //skip the complex part
}
// Keep only the lags we want and move negative lags before positive lags.
float[] c = new float[(mxl * 2) + 1];
System.arraycopy(c1, 0, c, mxl, mxl + 1); // +1 to place the 1.0 in the middle of correlation
System.arraycopy(c1, n2 - mxl, c, 0, mxl);
return c;
}
//TODO: findPeaks
}

2
android/ConductorsWatch/build.gradle
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@@ -7,7 +7,7 @@ buildscript {
jcenter()
}
dependencies {
classpath 'com.android.tools.build:gradle:3.0.0'
classpath 'com.android.tools.build:gradle:3.0.1'
// NOTE: Do not place your application dependencies here; they belong

6
octave/AutoCorrMethodNew_Watch.m
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@@ -84,9 +84,9 @@ for i = window_size+1:length(data)
if(mod(i,overlap) == 0)
%measure periodicity of window and use axis with best periodicity
[corr_x, lag_x] = xcov(m(i-window_size:i,3), (window_size/4), "coeff");
[corr_y, lag_y] = xcov(m(i-window_size:i,4), (window_size/4), "coeff");
[corr_z, lag_z] = xcov(m(i-window_size:i,5), (window_size/4), "coeff");
[corr_x, lag_x] = xcov(m(i-window_size:i,3), (window_size/2), "coeff");
[corr_y, lag_y] = xcov(m(i-window_size:i,4), (window_size/2), "coeff");
[corr_z, lag_z] = xcov(m(i-window_size:i,5), (window_size/2), "coeff");
corr_x_pos = corr_x;
corr_y_pos = corr_y;