负载(“network_Audio_SequentialFeatureSelection.mat”，“bestNet”，“安全的”，“标准化者”）;

fs = afe.SampleRate;deviceReader = audioDeviceReader(SampleRate=fs,SamplesPerFrame=256);audioBuffer = dsp.AsyncBuffer(fs*3);steBuffer = dsp.AsyncBuffer(1000);predictionBuffer = dsp.AsyncBuffer(5);

图;streamAxes = subplot(3,1,1);streamPlot = plot(零(fs,1));ylabel (“振幅”)包含(“时间(s)”)标题(“音频流”) streamAxes。XTick = [0,fs];streamAxes。XTickLabel = [0,1];streamAxes。YLim = [-1,1];analyzedAxes = subplot(3,1,2);analyzedPlot = plot(零(fs/2,1));标题(“分析”) ylabel (“振幅”)包含(“时间(s)”) set(gca,XTickLabel=[]) analyzedAxes。XTick = [0,fs/2];analyzedAxes。XTickLabel = [0,0.5];analyzedAxes。YLim = [-1,1];probabilityAxes = subplot(3,1,3);probabilityPlot = bar(0:9,0.1*ones(1,10));轴([1,10,0,1])ylabel (“概率”)包含(“类”）

steThreshold = 0.015;idxVec = 1:fs;抽搐而Toc < 20从您的设备读取一帧音频。audioIn = deviceReader();将音频写入缓冲区。写(audioBuffer audioIn);%当200毫秒的数据未使用时，继续此循环。而audioBuffer。NumUnreadSamples > 0.2*fs从音频缓冲区读取1秒。在这一秒中，有800毫秒%是重读旧数据，200 ms是新数据。audioToAnalyze = read(audioBuffer,fs,0.8*fs);更新图形以绘制当前音频数据。streamPlot。YData = audioToAnalyze;ste = mean(abs(audioToAnalyze));写(steBuffer, ste);如果steBuffer。NumUnreadSamples > 5 abc = sort(peek(steBuffer));steThreshold = abc(round(0.4* nummel (abc)));结束如果ste > steThreshold使用detectspeech函数来确定一个语音区域%存在。idx = detectSpeech(audioToAnalyze,fs);如果存在一个语音区域，执行以下操作。如果~ isempty (idx)除语音外，把信号的所有部分归零%区域，并修剪到0.5秒。audioToAnalyze = trimOrPad(audioToAnalyze(idx(1,1):idx(1,2)))，fs/2);正常化音频。audioToAnalyze = audioToAnalyze/max(abs(audioToAnalyze));更新分析的线段图analyzedPlot。YData = audioToAnalyze;提取特征和转置它们，使时间。%跨列。features = (extract(afe,audioToAnalyze))';使特征正常化。features = (features - normalizers.Mean) ./ normalizers.标准差;调用分类来确定概率和%获奖标签。Features (isnan(Features)) = 0;[label,probs] = category (bestNet,features);用概率和获胜来更新情节%的标签。probabilityPlot。YData = probs;写(predictionBuffer,聚合氯化铝);如果predictionBuffer。NumUnreadSamples == predictionBuffer。容量lastTen = peek(predictionBuffer);[~,决定]= max(平均(lastTen。*损害(大小(lastTen, 1)), 1));probabilityax . title . string = num2str(decision-1);结束结束其他的如果信号能量低于阈值，则假设无语音%检测。probabilityAxes.Title.String =＂＂；probabilityPlot。YData = 0.1*ones(10,1);analyzedPlot。YData = 0 (fs/2,1);重置(predictionBuffer)结束drawnowlimitrate结束结束

downloadFolder = matlab.internal.examples.download金宝appSupportFile(“音频”，“FSDD.zip”）;dataFolder = tempdir;unzip(下载文件夹，数据文件夹)dataset = fullfile(数据文件夹，数据文件夹)“FSDD”）;

ads = audioDatastore(dataset, inclesubfolders =true);[~，adsInfo] = read(ads);fs = adsInfo.SampleRate;

[~，filename] = cellfun(@(x)fileparts(x)，ads.Files,UniformOutput=false);ads.Labels = categorical(string(cellfun(@(x)x(1)，文件名));

[adsTrain,adsValidation] = splitEachLabel(ads,0.8);

赢=汉明(轮(0.03*fs)，“周期”）;overlapLength = round(0.02*fs);afe = audioFeatureExtractor(.．.窗口=赢,.．.OverlapLength = OverlapLength,.．.SampleRate = fs,.．..．.linearSpectrum = false,.．.melSpectrum = false,.．.barkSpectrum = false,.．.erbSpectrum = false,.．..．.mfcc = true,.．.mfccDelta = true,.．.mfccDeltaDelta = true,.．.gtcc = true,.．.gtccDelta = true,.．.gtccDeltaDelta = true,.．..．.spectralCentroid = true,.．.spectralCrest = true,.．.spectralDecrease = true,.．.spectralEntropy = true,.．.spectralFlatness = true,.．.spectralFlux = true,.．.spectralKurtosis = true,.．.spectralRolloffPoint = true,.．.spectralSkewness = true,.．.spectralSlope = true,.．.spectralSpread = true,.．..．.= false,.．.harmonicRatio = false,.．.zerocrossrate = false,.．.shortTimeEnergy = false);

numUnits =One hundred.；层= [.．.sequenceInputLayer (1) bilstmLayer (numUnits OutputMode =“最后一次”) fullyConnectedLayer(nummel (categories(adsTrain.Labels)) softmaxLayer classificationLayer];选项= trainingOptions(“亚当”，.．.LearnRateSchedule =“分段”，.．.洗牌=“every-epoch”，.．.Verbose = false,.．.MaxEpochs = 20);

方向=“前进”；[日志，bestFeatures,bestNet,normalizers] = sequentialfeaturesselection (adsTrain,adsValidation,afe,layers,options,direction);

日志

bestFeatures

安全设置(afe bestFeatures)

%保存(“network_Audio_SequentialFeatureSelection.mat”、“bestNet”,“安全的”,“标准化者”)

函数[trueLabels,predictedLabels,net,normalizers] = trainAndValidateNetwork(adsTrain,adsValidation,afe,layers,options)训练并验证网络。％%的输入:指向训练集的audioDatastore对象指向验证集的audioDatastore对象% afe - audioFeatureExtractor对象。% layers - LSTM或BiLSTM网络的层options - trainingOptions对象％%输出:% trueLabels -验证集的真实标签% predictedLabels -验证集的预测标签net训练过的网络% normalizers -被测特性的归一化因子The MathWorks, Inc.版权所有将数据转换为高数组。tallTrain = tall(adsTrain);tallValidation = tall(adsValidation);从训练集中提取特征。重新定位特征% time沿行与sequenceInputLayer兼容。fs = afe.SampleRate;tallTrain = cellfun(@(x)trimOrPad(x,fs/2)，tallTrain,UniformOutput=false);tallTrain = cellfun(@(x)x/max(abs(x)，[]，“所有”)、tallTrain UniformOutput = false);tallFeaturesTrain = cellfun(@(x)extract(afe,x)，tallTrain,UniformOutput=false);tallFeaturesTrain = cellfun(@(x)x'，tallFeaturesTrain,UniformOutput=false);% #好< NASGU >[~，featuresTrain] = evalc(“收集(tallFeaturesTrain)”）;%使用evalc抑制命令行输出。tallValidation = cellfun(@(x)trimOrPad(x,fs/2)，tallValidation,UniformOutput=false);tallValidation = cellfun(@(x)x/max(abs(x)，[]，“所有”)、tallValidation UniformOutput = false);tallFeaturesValidation = cellfun(@(x)extract(afe,x)，tallValidation,UniformOutput=false);tallFeaturesValidation = cellfun(@(x)x'，tallFeaturesValidation,UniformOutput=false);% #好< NASGU >[~，featuresValidation] = evalc(“收集(tallFeaturesValidation)”）;%使用evalc抑制命令行输出。使用训练集来确定每一个的平均值和标准差%的特性。规范化训练集和验证集。allFeatures = cat(2,featuresTrain{:});M = mean(allFeatures,2，“omitnan”）;S = std(allFeatures,0,2，“omitnan”）;featuresTrain = cellfun(@(x)(x- m)./S,featuresTrain,UniformOutput=false);为ii = 1:numel(featuresTrain) idx = find(isnan(featuresTrain{ii}));如果~isempty(idx) featuresTrain{ii}(idx) = 0;结束结束featuresValidation = cellfun(@(x)(x- m)./S,featuresValidation,UniformOutput=false);为ii = 1:numel(featuresValidation) idx = find(isnan(featuresValidation{ii}));如果~isempty(idx) featuresValidation{ii}(idx) = 0;结束结束复制训练和验证集的标签，使它们在%与序列一一对应。labelsTrain = adsTrain.Labels;更新输入层测试功能的数量。layers(1) = sequenceInputLayer(size(featuresTrain{1}，1));培训网络。net = trainNetwork(featuresTrain,labelsTrain,layers,options);评估网络。调用classification获取每个标签的预测标签%的序列。predictedLabels =分类(net,featuresValidation);trueLabels = adsValidation.Labels;将归一化因子保存为结构。标准化者。均值= M;标准化者。标准差= S;结束

函数[日志，bestFeatures,bestNet,bestNormalizers] = sequentialfeaturesselection (adsTrain,adsValidate,afeThis,layers,options,direction)％%的输入:指向训练集的audioDatastore对象指向验证集的audioDatastore对象% afe - audioFeatureExtractor对象。将所有要测试的特性设置为true% layers - LSTM或BiLSTM网络的层options - trainingOptions对象% direction - SFS方向，指定为'forward'或'backward'％%输出:日志簿-包含测试的特性配置和相应验证精度的表% bestFeatures -包含最佳特性配置的结构% bestNet -具有最高验证精度的训练网络% bestNormalizers -用于最佳特性的特征归一化因子The MathWorks, Inc.版权所有afe = copy(afeThis);featuresToTest = fieldnames(info(afe));N = numel(featuresToTest);bestValidationAccuracy = 0;设置初始特性配置:全部开启，用于向后选择%或全部关闭向前选择。featureConfig = info(afe);为i = 1:N如果strcmpi(方向,“落后”) featureConfig.(featuresToTest{i}) = true;其他的featureConfig.(featuresToTest{i}) = false;结束结束初始化日志以跟踪功能配置和准确性。。logbook = table(featureConfig,0,VariableNames=[“功能配置”，“准确性”]);执行顺序特性评估。wrapperIdx = 1;bestAccuracy = 0;而wrapperIdx <= N创建包含要测试的所有特性配置的单元格数组%在当前循环中。featureConfigsToTest = cell(numel(featuresToTest)，1);为ii = 1:数字(featuresToTest)如果strcmpi(方向,“落后”) featureConfig.(featuresToTest{ii}) = false;其他的featureConfig.(featuresToTest{ii}) = true;结束featureConfigsToTest{ii} = featureConfig;如果strcmpi(方向,“落后”) featureConfig.(featuresToTest{ii}) = true;其他的featureConfig.(featuresToTest{ii}) = false;结束结束遍历每个特性集。为ii = 1:数字(featureConfigsToTest)确定要测试的当前特性配置。。更新%功能安全。currentConfig = featureConfigsToTest{ii};currentConfig集(afe)%训练并获得电流的k倍交叉验证精度%特性配置。[trueLabels,predictedLabels,net,normalizers] = trainAndValidateNetwork(adsTrain,adsValidate,afe,layers,options);valAccuracy = mean(trueLabels==predictedLabels)*100;如果valAccuracy > bestValidationAccuracy = valAccuracy;bestNet =网络;bestNormalizers = normalizers;结束%更新日志result = table(currentConfig,valAccuracy,VariableNames=[“功能配置”，“准确性”]);日志日志=[日志日志;结果];% #好< AGROW >结束以最佳精度确定和打印设置。如果精度%没有改善，结束运行。[a,b] = max(日志{:，“准确性”});如果a <= bestAccuracy wrapperIdx = inf;其他的wrapperIdx = wrapperIdx + 1;结束bestAccuracy = a;根据最近的获胜者更新要测试的功能。。赢家=日志{b，“功能配置”};Fn = fieldnames(winner);Tf = structfun(@(x)(x)，赢家);如果strcmpi(方向,“落后”) featuresToRemove = fn(~tf);其他的featuresToRemove = fn(tf);结束为ii = 1: nummel (featurestomove) loc = strcmp(featuresToTest, featurerestoremove {ii});featuresToTest(loc) = [];如果strcmpi(方向,“落后”) featureConfig.(featurerestoremove {ii}) = false;其他的featureConfig.(featureoremove {ii}) = true;结束结束结束对日志进行排序，使其更具可读性。。日志(1，:)= [];删除占位符第一行。航海日志= sortrows(航海日志，“准确性”，“下”）;bestFeatures =日志{1，“功能配置”};M =日志{:，“功能配置”};Fn = fieldnames(m);myString =字符串(数字(m)，1);为wrapperIdx = 1:numel(m) tf = structfun(@(x)(x)，logbook{wrapperIdx，“功能配置”});myString(wrapperIdx) = strjoin(fn(tf)，”、“）;结束logbook = table(myString,logbook{:，“准确性”}, VariableNames = (“特征”，“准确性”]);结束

函数y = trimOrPad(x,n)% y = trimOrPad(x,n)修整或填充输入x到n个样本。如果x是%修剪，前面和后面修剪得一样。如果x是填充的，它就是%前面和后面都用零填充。用于奇数长度的修剪或%填充物，多余的样品从后面修剪或填充物。The MathWorks, Inc.版权所有A = size(x,1);如果a < n frontPad = floor((n-a)/2);backPad = n - a - frontPad;y = [0 (frontPad,1);x; 0 (backPad,1)];elseifa > n frontTrim =地板((a-n)/2)+1;backTrim = a - n - frontTrim;y = x(frontTrim:end-backTrim);其他的Y = x;结束结束