Silence Removal and End Point Detection JAVA Code（音频删除静音与结束判断）

转载自：http://ganeshtiwaridotcomdotnp.blogspot.com/2011/08/silence-removal-and-end-point-detection_29.html

For the purpose of silence removal of captured sound, we used the algorithm in our final year project.

In this post, I am publishing the endpoint detection and silence removal code ( implementation of this algorithm in JAVA).

These links might be useful to you as well.

• For Capturing audio from microphone
• For Converting captured data into PCM integer or float array
• For Playing any PCM array of amplitudes

The constructor of following java class EndPointDetection takes two parameters

1. array of original signal's amplitude data : float[] originalSignal
2. sampling rate of original signal in Hz : int samplingRate

package org.ioe.tprsa.audio.preProcessings;
/**
 * @author Ganesh Tiwari
 * @reference 'A New Silence Removal and Endpoint Detection Algorithm
 * for Speech and Speaker Recognition Applications' by IIT, Khragpur
 */
public class EndPointDetection {
    private float[] originalSignal; //input
    private float[] silenceRemovedSignal;//output
    private int samplingRate;
    private int firstSamples;
    private int samplePerFrame;
    public EndPointDetection(float[] originalSignal, int samplingRate) {
        this.originalSignal = originalSignal;
        this.samplingRate = samplingRate;
        samplePerFrame = this.samplingRate / 1000;
        firstSamples = samplePerFrame * 200;// according to formula
    }
    public float[] doEndPointDetection() {
        // for identifying each sample whether it is voiced or unvoiced
        float[] voiced = new float[originalSignal.length];
        float sum = 0;
        double sd = 0.0;
        double m = 0.0;
        // 1. calculation of mean
        for (int i = 0; i < firstSamples; i++) {
            sum += originalSignal[i];
        }
        m = sum / firstSamples;// mean
        sum = 0;// reuse var for S.D.

        // 2. calculation of Standard Deviation
        for (int i = 0; i < firstSamples; i++) {
            sum += Math.pow((originalSignal[i] - m), 2);
        }
        sd = Math.sqrt(sum / firstSamples);
        // 3. identifying one-dimensional Mahalanobis distance function
        // i.e. |x-u|/s greater than ####3 or not,
        for (int i = 0; i < originalSignal.length; i++) {
            if ((Math.abs(originalSignal[i] - m) / sd) > 0.3) { //0.3 =THRESHOLD.. adjust value yourself
                voiced[i] = 1;
            } else {
                voiced[i] = 0;
            }
        }
        // 4. calculation of voiced and unvoiced signals
        // mark each frame to be voiced or unvoiced frame
        int frameCount = 0;
        int usefulFramesCount = 1;
        int count_voiced = 0;
        int count_unvoiced = 0;
        int voicedFrame[] = new int[originalSignal.length / samplePerFrame];
        // the following calculation truncates the remainder
        int loopCount = originalSignal.length - (originalSignal.length % samplePerFrame);
        for (int i = 0; i < loopCount; i += samplePerFrame) {
            count_voiced = 0;
            count_unvoiced = 0;
            for (int j = i; j < i + samplePerFrame; j++) {
                if (voiced[j] == 1) {
                    count_voiced++;
                } else {
                    count_unvoiced++;
                }
            }
            if (count_voiced > count_unvoiced) {
                usefulFramesCount++;
                voicedFrame[frameCount++] = 1;
            } else {
                voicedFrame[frameCount++] = 0;
            }
        }
        // 5. silence removal
        silenceRemovedSignal = new float[usefulFramesCount * samplePerFrame];
        int k = 0;
        for (int i = 0; i < frameCount; i++) {
            if (voicedFrame[i] == 1) {
                for (int j = i * samplePerFrame; j < i * samplePerFrame + samplePerFrame; j++) {
                    silenceRemovedSignal[k++] = originalSignal[j];
                }
            }
        }
        // end
        return silenceRemovedSignal;
    }
}

The MATLAB implementation of this algorithm is also available.

问：Hi ganesh, So Is impossible listen the voice after normalizePCM and endpointdetection?

答：you can play the recorded audio after doing those time domain operations.
　　you need to play the pcm array using the code : http://ganeshtiwaridotcomdotnp.blogspot.com/2011/12/java-audio-playing-pcm-amplitude-array.html

　　you can find other codes related to sound processing in java here :
　　http://ganeshtiwaridotcomdotnp.blogspot.com/search/label/Audio%20Processing