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  • 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.

    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

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  • 原文地址:https://www.cnblogs.com/passedbylove/p/11888679.html
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