Updated code for 20a release

Author: davidwillingham

HelperFiles/DownloadData.m

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+%   Copyright 2018-2020 The MathWorks, Inc.
+
+function OpenCodegenReport
+
+prj = currentProject;
+open(fullfile(prj.RootFolder,'arm_compute','html','report.mldatx'));
+
+end

HelperFiles/OpenCodegenReport.m

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+%   Copyright 2020 The MathWorks, Inc.
+
+function features = extractFeatures(audioIn,fs)
+    
+    %#codegen
+    
+    persistent stf F winPower
+
+    if(isempty(stf))   
+        % Setup a short time fourier transform object
+        FFTLength = 512;
+        windowLength = 512;
+        hopLength = 512;
+        win = hamming(windowLength,'periodic');
+        winPower =  2/(sum(win)^2);
+        stf = dsp.STFT(win,windowLength-hopLength,FFTLength);
+        [~,temp] = stft(zeros(windowLength,1),fs,'Window',win,'OverlapLength',windowLength-hopLength,'centered',false);
+        F = temp(1:257);
+    end
+
+    Xcomp = stf(audioIn);
+    Xcomp= Xcomp(1:257,:);
+    
+    % Get power spectrum
+    X = real(Xcomp.*conj(Xcomp));
+    
+    % Normalize by window power 
+    X = X .* winPower;
+    
+    X(1,:) = 0.5*X(1,:);
+    X(end,:) = 0.5*X(end,:);
+
+    S1 = spectralCentroid(X,F);
+    S2 = spectralCrest(X,F);
+    S3 = spectralDecrease(X,F);
+    S4 = spectralEntropy(X,F);
+    S5 = spectralFlatness(X,F);
+%     S6 = spectralFlux(X,F);
+    S7  = spectralKurtosis(X,F);
+    S8  = spectralRolloffPoint(X,F);
+    S9  = spectralSkewness(X,F);
+    S10 = spectralSlope(X,F);
+    S11 = spectralSpread(X,F);
+
+    features = [S1 S2 S3 S4 S5 S7 S8 S9 S10 S11];
+
+end
+

HelperFiles/OpenPart1.m

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+%   Copyright 2020 The MathWorks, Inc.
+
+% Recreate the datastore again
+
+dataFolder = 'AirCompressorData';
+ads = audioDatastore(dataFolder,'IncludeSubfolders',true,'LabelSource','foldernames');
+rng(3);
+ads = shuffle(ads);
+[adsTrain,adsValidation] = splitEachLabel(ads,0.9,0.1);

HelperFiles/OpenPart2.m

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+%   Copyright 2020 The MathWorks, Inc.
+
+function [scores,features] = streamingClassifier(audioIn,M,S)
+% This is a streaming classifier function 
+
+persistent airCompNet; 
+
+if isempty(airCompNet)
+    airCompNet = coder.loadDeepLearningNetwork('TrainedModel.mat');
+end
+
+% Extract features using function
+features = extractFeatures(audioIn,16e3);
+
+% Normalize 
+features = single(((features - M)./S))';
+
+% Classify
+[airCompNet, scores] = predictAndUpdateState(airCompNet,features);
+
+end
+