/*
* @(#)RMS.java 0.5 1.0 April 5, 2005.
*
* McGill Univarsity
*/
package org.vocvark.AudioFeatures;
import org.vocvark.DataTypes.FeatureDefinition;
/**
* A feature extractor that extracts the Root Mean Square (RMS) from a set of
* samples. This is a good measure of the power of a signal.
* <p/>
* <p>RMS is calculated by summing the squares of each sample, dividing this
* by the number of samples in the window, and finding the square root of the
* result.
* <p/>
* <p>No extracted feature values are stored in objects of this class.
*
* @author Cory McKay
*/
public class RMS
extends FeatureExtractorBaseImpl {
/* CONSTRUCTOR **************************************************************/
/**
* Basic constructor that sets the definition and dependencies (and their
* offsets) of this feature.
*/
public RMS() {
String name = "Root Mean Square";
String description = "A measure of the power of a signal.";
boolean is_sequential = true;
int dimensions = 1;
definition = new FeatureDefinition(name,
description,
is_sequential,
dimensions);
dependencies = null;
offsets = null;
}
/* PUBLIC METHODS **********************************************************/
/**
* Extracts this feature from the given samples at the given sampling
* rate and given the other feature values.
* <p/>
* <p>In the case of this feature, the sampling_rate and
* other_feature_values parameters are ignored.
*
* @param samples The samples to extract the feature from.
* @param sampling_rate The sampling rate that the samples are
* encoded with.
* @param other_feature_values The values of other features that are
* needed to calculate this value. The
* order and offsets of these features
* must be the same as those returned by
* this class's getDependencies and
* getDependencyOffsets methods respectively.
* The first indice indicates the feature/window
* and the second indicates the value.
* @throws Exception Throws an informative exception if
* the feature cannot be calculated.
* @return The extracted feature value(s).
*/
public double[] extractFeature(double[] samples,
double sampling_rate,
double[][] other_feature_values)
throws Exception {
double sum = 0.0;
for (int samp = 0; samp < samples.length; samp++)
sum += Math.pow(samples[samp], 2);
double rms = Math.sqrt(sum / samples.length);
double[] result = new double[1];
result[0] = rms;
return result;
}
/**
* Create an identical copy of this feature. This permits FeatureExtractor
* to use the prototype pattern to create new composite features using
* metafeatures.
*/
public Object clone() {
return new RMS();
}
}