MLECO-2599: Replace DSCNN with MicroNet for KWS

Added SoftMax function to Mathutils to allow MicroNet
to output probability as it does not nativelu have this layer.
Minor refactoring to accommodate Softmax Calculations
Extensive renaming and updating of documentation and resource download script.
Added SoftMax function to Mathutils to allow MicroNet
to output probability.

Change-Id: I7cbbda1024d14b85c9ac1beea7ca8fbffd0b6eb5
Signed-off-by: Liam Barry <liam.barry@arm.com>

1 files changed, 10 insertions, 10 deletions

diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index 1d88ba1..c67be22 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -20,8 +20,8 @@
 #include "InputFiles.hpp"
 #include "AudioUtils.hpp"
 #include "UseCaseCommonUtils.hpp"
-#include "DsCnnModel.hpp"
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsModel.hpp"
+#include "MicroNetKwsMfcc.hpp"
 #include "Classifier.hpp"
 #include "KwsResult.hpp"
 #include "Wav2LetterMfcc.hpp"
@@ -77,12 +77,12 @@ namespace app {
      *
      * @param[in]           mfcc            MFCC feature calculator.
      * @param[in,out]       inputTensor     Input tensor pointer to store calculated features.
-     * @param[in]            cacheSize      Size of the feture vectors cache (number of feature vectors).
+     * @param[in]           cacheSize       Size of the feature vectors cache (number of feature vectors).
      *
      * @return function     function to be called providing audio sample and sliding window index.
      **/
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::DsCnnMFCC&  mfcc,
+    GetFeatureCalculator(audio::MicroNetMFCC&  mfcc,
                          TfLiteTensor*      inputTensor,
                          size_t             cacheSize);
 
@@ -98,8 +98,8 @@ namespace app {
         constexpr uint32_t dataPsnTxtInfStartY = 40;
 
         constexpr int minTensorDims = static_cast<int>(
-            (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)?
-             arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx);
+            (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
+             arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
 
         KWSOutput output;
 
@@ -128,7 +128,7 @@ namespace app {
         const uint32_t kwsNumMfccFeats = ctx.Get<uint32_t>("kwsNumMfcc");
         const uint32_t kwsNumAudioWindows = ctx.Get<uint32_t>("kwsNumAudioWins");
 
-        audio::DsCnnMFCC kwsMfcc = audio::DsCnnMFCC(kwsNumMfccFeats, kwsFrameLength);
+        audio::MicroNetMFCC kwsMfcc = audio::MicroNetMFCC(kwsNumMfccFeats, kwsFrameLength);
         kwsMfcc.Init();
 
         /* Deduce the data length required for 1 KWS inference from the network parameters. */
@@ -152,7 +152,7 @@ namespace app {
 
         /* We expect to be sampling 1 second worth of data at a time
          * NOTE: This is only used for time stamp calculation. */
-        const float kwsAudioParamsSecondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq;
+        const float kwsAudioParamsSecondsPerSample = 1.0/audio::MicroNetMFCC::ms_defaultSamplingFreq;
 
         auto currentIndex = ctx.Get<uint32_t>("clipIndex");
 
@@ -230,7 +230,7 @@ namespace app {
 
             kwsClassifier.GetClassificationResults(
                             kwsOutputTensor, kwsClassificationResult,
-                            ctx.Get<std::vector<std::string>&>("kwslabels"), 1);
+                            ctx.Get<std::vector<std::string>&>("kwslabels"), 1, true);
 
             kwsResults.emplace_back(
                 kws::KwsResult(
@@ -604,7 +604,7 @@ namespace app {
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+    GetFeatureCalculator(audio::MicroNetMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
     {
         std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;