diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index 3d95753..8085af7 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -18,9 +18,9 @@
 
 #include "InputFiles.hpp"
 #include "Classifier.hpp"
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "hal.h"
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsMfcc.hpp"
 #include "AudioUtils.hpp"
 #include "UseCaseCommonUtils.hpp"
 #include "KwsResult.hpp"
@@ -59,7 +59,7 @@
      * @return          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::MicroNetKwsMFCC&  mfcc,
                                  TfLiteTensor*      inputTensor,
                                  size_t             cacheSize);
 
@@ -72,8 +72,8 @@
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         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);
 
         auto& model = ctx.Get<Model&>("model");
 
@@ -105,10 +105,10 @@
         }
 
         TfLiteIntArray* inputShape = model.GetInputShape(0);
-        const uint32_t kNumCols = inputShape->data[arm::app::DsCnnModel::ms_inputColsIdx];
-        const uint32_t kNumRows = inputShape->data[arm::app::DsCnnModel::ms_inputRowsIdx];
+        const uint32_t kNumCols = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
+        const uint32_t kNumRows = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
 
-        audio::DsCnnMFCC mfcc = audio::DsCnnMFCC(kNumCols, frameLength);
+        audio::MicroNetKwsMFCC mfcc = audio::MicroNetKwsMFCC(kNumCols, frameLength);
         mfcc.Init();
 
         /* Deduce the data length required for 1 inference from the network parameters. */
@@ -132,7 +132,7 @@
 
         /* We expect to be sampling 1 second worth of data at a time.
          * NOTE: This is only used for time stamp calculation. */
-        const float secondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq;
+        const float secondsPerSample = 1.0/audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
 
         do {
             platform.data_psn->clear(COLOR_BLACK);
@@ -208,7 +208,7 @@
                 std::vector<ClassificationResult> classificationResult;
                 auto& classifier = ctx.Get<KwsClassifier&>("classifier");
                 classifier.GetClassificationResults(outputTensor, classificationResult,
-                                                    ctx.Get<std::vector<std::string>&>("labels"), 1);
+                                                    ctx.Get<std::vector<std::string>&>("labels"), 1, true);
 
                 results.emplace_back(kws::KwsResult(classificationResult,
                     audioDataSlider.Index() * secondsPerSample * audioDataStride,
@@ -240,7 +240,6 @@
         return true;
     }
 
-    
     static bool PresentInferenceResult(hal_platform& platform,
                                        const std::vector<arm::app::kws::KwsResult>& results)
     {
@@ -259,7 +258,6 @@
 
             std::string topKeyword{"<none>"};
             float score = 0.f;
-
             if (!results[i].m_resultVec.empty()) {
                 topKeyword = results[i].m_resultVec[0].m_label;
                 score = results[i].m_resultVec[0].m_normalisedVal;
@@ -366,7 +364,7 @@
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+    GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
     {
         std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;