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/*
* Copyright (C) 2015 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.drrickorang.loopback;
import android.util.Log;
/**
* This class is used to automatically estimate latency and its confidence.
*/
public class Correlation {
private static final String TAG = "Correlation";
private int mBlockSize = 4096;
private int mSamplingRate;
private double [] mDataDownsampled = new double [mBlockSize];
private double [] mDataAutocorrelated = new double[mBlockSize];
public double mEstimatedLatencySamples = 0;
public double mEstimatedLatencyMs = 0;
public double mEstimatedLatencyConfidence = 0.0;
private double mAmplitudeThreshold = 0.001; // 0.001 = -60 dB noise
private boolean mDataIsValid = false; // Used to mark computed latency information is available
public void init(int blockSize, int samplingRate) {
mBlockSize = blockSize;
mSamplingRate = samplingRate;
}
public void computeCorrelation(double [] data, int samplingRate) {
log("Started Auto Correlation for data with " + data.length + " points");
mSamplingRate = samplingRate;
downsampleData(data, mDataDownsampled, mAmplitudeThreshold);
//correlation vector
autocorrelation(mDataDownsampled, mDataAutocorrelated);
int N = data.length; //all samples available
double groupSize = (double) N / mBlockSize; //samples per downsample point.
double maxValue = 0;
int maxIndex = -1;
double minLatencyMs = 8; //min latency expected. This algorithm should be improved.
int minIndex = (int) (0.5 + minLatencyMs * mSamplingRate / (groupSize * 1000));
double average = 0;
double rms = 0;
//find max
for (int i = minIndex; i < mDataAutocorrelated.length; i++) {
average += mDataAutocorrelated[i];
rms += mDataAutocorrelated[i] * mDataAutocorrelated[i];
if (mDataAutocorrelated[i] > maxValue) {
maxValue = mDataAutocorrelated[i];
maxIndex = i;
}
}
rms = Math.sqrt(rms / mDataAutocorrelated.length);
average = average / mDataAutocorrelated.length;
log(String.format(" Maxvalue %f, max Index : %d/%d (%d) minIndex = %d", maxValue, maxIndex,
mDataAutocorrelated.length, data.length, minIndex));
log(String.format(" average : %.3f rms: %.3f", average, rms));
mEstimatedLatencyConfidence = 0.0;
if (average > 0) {
double factor = 3.0;
double raw = (rms - average) / (factor * average);
log(String.format("Raw: %.3f", raw));
mEstimatedLatencyConfidence = Math.max(Math.min(raw, 1.0), 0.0);
}
log(String.format(" ****Confidence: %.2f", mEstimatedLatencyConfidence));
mEstimatedLatencySamples = maxIndex * groupSize;
mEstimatedLatencyMs = mEstimatedLatencySamples * 1000 / mSamplingRate;
log(String.format(" latencySamples: %.2f %.2f ms", mEstimatedLatencySamples,
mEstimatedLatencyMs));
mDataIsValid = mEstimatedLatencyMs > 0.0001;
}
// Called by LoopbackActivity before displaying latency test results
public boolean isValid() {
return mDataIsValid;
}
// Called at beginning of new test
public void invalidate() {
mDataIsValid = false;
}
private boolean downsampleData(double [] data, double [] dataDownsampled, double threshold) {
boolean status;
for (int i = 0; i < mBlockSize; i++) {
dataDownsampled[i] = 0;
}
int N = data.length; //all samples available
double groupSize = (double) N / mBlockSize;
int ignored = 0;
int currentIndex = 0;
double nextGroup = groupSize;
for (int i = 0; i < N && currentIndex < mBlockSize; i++) {
if (i > nextGroup) { //advanced to next group.
currentIndex++;
nextGroup += groupSize;
}
if (currentIndex >= mBlockSize) {
break;
}
double value = Math.abs(data[i]);
if (value >= threshold) {
dataDownsampled[currentIndex] += value;
} else {
ignored++;
}
}
log(String.format(" Threshold: %.3f, ignored:%d/%d (%%.2f)",
threshold, ignored, N, (double) ignored/(double)N));
status = true;
return status;
}
private boolean autocorrelation(double [] data, double [] dataOut) {
boolean status = false;
double sumsquared = 0;
int N = data.length;
for (int i = 0; i < N; i++) {
double value = data[i];
sumsquared += value * value;
}
if (sumsquared > 0) {
//correlate (not circular correlation)
for (int i = 0; i < N; i++) {
dataOut[i] = 0;
for (int j = 0; j < N - i; j++) {
dataOut[i] += data[j] * data[i + j];
}
dataOut[i] = dataOut[i] / sumsquared;
}
status = true;
}
return status;
}
private static void log(String msg) {
Log.v(TAG, msg);
}
}
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