MLECO-3173: Add AD, KWS_ASR and Noise reduction use case API's

Signed-off-by: Richard Burton <richard.burton@arm.com>

Change-Id: I36f61ce74bf17f7b327cdae9704a22ca54144f37

5 files changed, 151 insertions, 186 deletions

diff --git a/tests/use_case/ad/PostProcessTests.cc b/tests/use_case/ad/PostProcessTests.cc
deleted file mode 100644
index 62fa9e7..0000000
--- a/tests/use_case/ad/PostProcessTests.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "AdPostProcessing.hpp"
-#include <catch.hpp>
-
-TEST_CASE("Softmax_vector") {
-
-    std::vector<float> testVec = {1, 2, 3, 4, 1, 2, 3};
-    arm::app::Softmax(testVec);
-    CHECK((testVec[0] - 0.024) == Approx(0.0).margin(0.001));
-    CHECK((testVec[1] - 0.064) == Approx(0.0).margin(0.001));
-    CHECK((testVec[2] - 0.175) == Approx(0.0).margin(0.001));
-    CHECK((testVec[3] - 0.475) == Approx(0.0).margin(0.001));
-    CHECK((testVec[4] - 0.024) == Approx(0.0).margin(0.001));
-    CHECK((testVec[5] - 0.064) == Approx(0.0).margin(0.001));
-    CHECK((testVec[6] - 0.175) == Approx(0.0).margin(0.001));
-}
-
-TEST_CASE("Output machine index") {
-
-    auto index = arm::app::OutputIndexFromFileName("test_id_00.wav");
-    CHECK(index == 0);
-
-    auto index1 = arm::app::OutputIndexFromFileName("test_id_02.wav");
-    CHECK(index1 == 1);
-
-    auto index2 = arm::app::OutputIndexFromFileName("test_id_4.wav");
-    CHECK(index2 == 2);
-
-    auto index3 = arm::app::OutputIndexFromFileName("test_id_6.wav");
-    CHECK(index3 == 3);
-
-    auto index4 = arm::app::OutputIndexFromFileName("test_id_id_00.wav");
-    CHECK(index4 == -1);
-
-    auto index5 = arm::app::OutputIndexFromFileName("test_id_7.wav");
-    CHECK(index5 == -1);
-}
\ No newline at end of file
diff --git a/tests/use_case/kws_asr/MfccTests.cc b/tests/use_case/kws_asr/MfccTests.cc
index 3ebdcf4..883c215 100644
--- a/tests/use_case/kws_asr/MfccTests.cc
+++ b/tests/use_case/kws_asr/MfccTests.cc
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -93,13 +93,13 @@ const std::vector<float> testWavMfcc {
     -22.67135, -0.61615, 2.07233, 0.58137, 1.01655, 0.85816, 0.46039, 0.03393, 1.16511, 0.0072,
 };
 
-arm::app::audio::MicroNetMFCC GetMFCCInstance() {
-    const int sampFreq = arm::app::audio::MicroNetMFCC::ms_defaultSamplingFreq;
+arm::app::audio::MicroNetKwsMFCC GetMFCCInstance() {
+    const int sampFreq = arm::app::audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
     const int frameLenMs = 40;
     const int frameLenSamples = sampFreq * frameLenMs * 0.001;
     const int numMfccFeats = 10;
 
-   return arm::app::audio::MicroNetMFCC(numMfccFeats, frameLenSamples);
+   return arm::app::audio::MicroNetKwsMFCC(numMfccFeats, frameLenSamples);
 }
 
 template <class T>
diff --git a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
index 6fd7df3..e343b66 100644
--- a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
+++ b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,15 +16,17 @@
  */
 #include "Wav2LetterPostprocess.hpp"
 #include "Wav2LetterModel.hpp"
+#include "ClassificationResult.hpp"
 
 #include <algorithm>
 #include <catch.hpp>
 #include <limits>
 
 template <typename T>
-static TfLiteTensor GetTestTensor(std::vector <int>& shape,
-                                  T                  initVal,
-                                  std::vector<T>&    vectorBuf)
+static TfLiteTensor GetTestTensor(
+        std::vector<int>&      shape,
+        T                      initVal,
+        std::vector<T>&        vectorBuf)
 {
     REQUIRE(0 != shape.size());
 
@@ -38,91 +40,112 @@ static TfLiteTensor GetTestTensor(std::vector <int>& shape,
     vectorBuf = std::vector<T>(sizeInBytes, initVal);
     TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(shape.data());
     return tflite::testing::CreateQuantizedTensor(
-                                vectorBuf.data(), dims,
-                                1, 0, "test-tensor");
+            vectorBuf.data(), dims,
+            1, 0, "test-tensor");
 }
 
 TEST_CASE("Checking return value")
 {
     SECTION("Mismatched post processing parameters and tensor size")
     {
-        const uint32_t ctxLen = 5;
-        const uint32_t innerLen = 3;
-        arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
+        const uint32_t outputCtxLen = 5;
+        arm::app::AsrClassifier classifier;
+        arm::app::Wav2LetterModel model;
+        model.Init();
+        std::vector<std::string> dummyLabels = {"a", "b", "$"};
+        const uint32_t blankTokenIdx = 2;
+        std::vector<arm::app::ClassificationResult> dummyResult;
         std::vector <int> tensorShape = {1, 1, 1, 13};
         std::vector <int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(
-                                tensorShape, 100, tensorVec);
-        REQUIRE(false == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+                tensorShape, 100, tensorVec);
+
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+                                      blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
+
+        REQUIRE(!post.DoPostProcess());
     }
 
     SECTION("Post processing succeeds")
     {
-        const uint32_t ctxLen = 5;
-        const uint32_t innerLen = 3;
-        arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
-        std::vector <int> tensorShape = {1, 1, 13, 1};
-        std::vector <int8_t> tensorVec;
+        const uint32_t outputCtxLen = 5;
+        arm::app::AsrClassifier classifier;
+        arm::app::Wav2LetterModel model;
+        model.Init();
+        std::vector<std::string> dummyLabels = {"a", "b", "$"};
+        const uint32_t blankTokenIdx = 2;
+        std::vector<arm::app::ClassificationResult> dummyResult;
+        std::vector<int> tensorShape = {1, 1, 13, 1};
+        std::vector<int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(
-                                tensorShape, 100, tensorVec);
+                tensorShape, 100, tensorVec);
+
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+                                      blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
-        std::vector <int8_t> originalVec = tensorVec;
+        std::vector<int8_t> originalVec = tensorVec;
 
         /* This step should not erase anything. */
-        REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+        REQUIRE(post.DoPostProcess());
     }
 }
 
+
 TEST_CASE("Postprocessing - erasing required elements")
 {
-    constexpr uint32_t ctxLen = 5;
+    constexpr uint32_t outputCtxLen = 5;
     constexpr uint32_t innerLen = 3;
-    constexpr uint32_t nRows = 2*ctxLen + innerLen;
+    constexpr uint32_t nRows = 2*outputCtxLen + innerLen;
     constexpr uint32_t nCols = 10;
     constexpr uint32_t blankTokenIdx = nCols - 1;
-    std::vector <int> tensorShape = {1, 1, nRows, nCols};
+    std::vector<int> tensorShape = {1, 1, nRows, nCols};
+    arm::app::AsrClassifier classifier;
+    arm::app::Wav2LetterModel model;
+    model.Init();
+    std::vector<std::string> dummyLabels = {"a", "b", "$"};
+    std::vector<arm::app::ClassificationResult> dummyResult;
 
     SECTION("First and last iteration")
     {
-        arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-        std::vector <int8_t> tensorVec;
-        TfLiteTensor tensor = GetTestTensor<int8_t>(
-                                tensorShape, 100, tensorVec);
+        std::vector<int8_t> tensorVec;
+        TfLiteTensor tensor = GetTestTensor<int8_t>(tensorShape, 100, tensorVec);
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+                                      blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
-        std::vector <int8_t> originalVec = tensorVec;
+        std::vector<int8_t>originalVec = tensorVec;
 
         /* This step should not erase anything. */
-        REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+        post.m_lastIteration = true;
+        REQUIRE(post.DoPostProcess());
         REQUIRE(originalVec == tensorVec);
     }
 
     SECTION("Right context erase")
     {
-        arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
         std::vector <int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(
-                                tensorShape, 100, tensorVec);
+                tensorShape, 100, tensorVec);
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+                                      blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
-        std::vector <int8_t> originalVec = tensorVec;
+        std::vector<int8_t> originalVec = tensorVec;
 
         /* This step should erase the right context only. */
-        REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+        post.m_lastIteration = false;
+        REQUIRE(post.DoPostProcess());
         REQUIRE(originalVec != tensorVec);
 
         /* The last ctxLen * 10 elements should be gone. */
-        for (size_t i = 0; i < ctxLen; ++i) {
+        for (size_t i = 0; i < outputCtxLen; ++i) {
             for (size_t j = 0; j < nCols; ++j) {
-                /* Check right context elements are zeroed. */
+                /* Check right context elements are zeroed. Blank token idx should be set to 1 when erasing. */
                 if (j == blankTokenIdx) {
-                    CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 1);
+                    CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
                 } else {
-                    CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 0);
+                    CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
                 }
 
                 /* Check left context is preserved. */
@@ -131,45 +154,47 @@ TEST_CASE("Postprocessing - erasing required elements")
         }
 
         /* Check inner elements are preserved. */
-        for (size_t i = ctxLen * nCols; i < (ctxLen + innerLen) * nCols; ++i) {
+        for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
             CHECK(tensorVec[i] == originalVec[i]);
         }
     }
 
     SECTION("Left and right context erase")
     {
-        arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
         std::vector <int8_t> tensorVec;
-        TfLiteTensor tensor = GetTestTensor<int8_t>(tensorShape, 100, tensorVec);
+        TfLiteTensor tensor = GetTestTensor<int8_t>(
+                tensorShape, 100, tensorVec);
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+                                      blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
         std::vector <int8_t> originalVec = tensorVec;
 
         /* This step should erase right context. */
-        REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+        post.m_lastIteration = false;
+        REQUIRE(post.DoPostProcess());
 
         /* Calling it the second time should erase the left context. */
-        REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+        REQUIRE(post.DoPostProcess());
 
         REQUIRE(originalVec != tensorVec);
 
         /* The first and last ctxLen * 10 elements should be gone. */
-        for (size_t i = 0; i < ctxLen; ++i) {
+        for (size_t i = 0; i < outputCtxLen; ++i) {
             for (size_t j = 0; j < nCols; ++j) {
                 /* Check left and right context elements are zeroed. */
                 if (j == blankTokenIdx) {
-                    CHECK(tensorVec[(ctxLen + innerLen) * nCols + i * nCols + j] == 1);
-                    CHECK(tensorVec[i * nCols + j] == 1);
+                    CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
+                    CHECK(tensorVec[i*nCols + j] == 1);
                 } else {
-                    CHECK(tensorVec[(ctxLen + innerLen) * nCols + i * nCols + j] == 0);
-                    CHECK(tensorVec[i * nCols + j] == 0);
+                    CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
+                    CHECK(tensorVec[i*nCols + j] == 0);
                 }
             }
         }
 
         /* Check inner elements are preserved. */
-        for (size_t i = ctxLen * nCols; i < (ctxLen + innerLen) * nCols; ++i) {
+        for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
             /* Check left context is preserved. */
             CHECK(tensorVec[i] == originalVec[i]);
         }
@@ -177,18 +202,21 @@ TEST_CASE("Postprocessing - erasing required elements")
 
     SECTION("Try left context erase")
     {
-        /* Should not be able to erase the left context if it is the first iteration. */
-        arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
         std::vector <int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(
-                                tensorShape, 100, tensorVec);
+                tensorShape, 100, tensorVec);
+
+        /* Should not be able to erase the left context if it is the first iteration. */
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+                                      blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
         std::vector <int8_t> originalVec = tensorVec;
 
         /* Calling it the second time should erase the left context. */
-        REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+        post.m_lastIteration = true;
+        REQUIRE(post.DoPostProcess());
+
         REQUIRE(originalVec == tensorVec);
     }
-}
\ No newline at end of file
+}
diff --git a/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
index 26ddb24..372152d 100644
--- a/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
+++ b/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,64 +16,54 @@
  */
 #include "Wav2LetterPreprocess.hpp"
 
-#include <algorithm>
-#include <catch.hpp>
 #include <limits>
+#include <catch.hpp>
 
 constexpr uint32_t numMfccFeatures = 13;
 constexpr uint32_t numMfccVectors  = 10;
 
 /* Test vector output: generated using test-asr-preprocessing.py. */
-int8_t expectedResult[numMfccVectors][numMfccFeatures*3] = {
-    /* Feature vec 0. */
-    -32,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,    /* MFCCs.   */
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,    /* Delta 1. */
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,    /* Delta 2. */
-
-    /* Feature vec 1. */
-    -31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
-    /* Feature vec 2. */
-    -31,   4,  -9,  -9, -10, -10, -11, -11, -11, -11, -12, -12, -12,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
-    /* Feature vec 3. */
-    -31,   4,  -9,  -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
-    /* Feature vec 4 : this should have valid delta 1 and delta 2. */
-    -31,   4,  -9,  -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
-    -38, -29,  -9,   1,  -2,  -7,  -8,  -8, -12, -16, -14,  -5,   5,
-    -68, -50, -13,   5,   0,  -9,  -9,  -8, -13, -20, -19,  -3,  15,
-
-    /* Feature vec 5 : this should have valid delta 1 and delta 2. */
-    -31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -11, -12, -12,
-    -62, -45, -11,   5,   0,  -8,  -9,  -8, -12, -19, -17,  -3,  13,
-    -27, -22, -13,  -9, -11, -12, -12, -11, -11, -13, -13, -10,  -6,
-
-    /* Feature vec 6. */
-    -31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
-    /* Feature vec 7. */
-    -32,   4,  -9,  -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
-    /* Feature vec 8. */
-    -32,   4,  -9,  -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
-    /* Feature vec 9. */
-    -31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
-    -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-    -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10
+int8_t expectedResult[numMfccVectors][numMfccFeatures * 3] = {
+        /* Feature vec 0. */
+        {-32,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,    /* MFCCs.   */
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,     /* Delta 1. */
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},    /* Delta 2. */
+        /* Feature vec 1. */
+        {-31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+        /* Feature vec 2. */
+        {-31,   4,  -9,  -9, -10, -10, -11, -11, -11, -11, -12, -12, -12,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+        /* Feature vec 3. */
+        {-31,   4,  -9,  -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+        /* Feature vec 4 : this should have valid delta 1 and delta 2. */
+        {-31,   4,  -9,  -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
+                -38, -29,  -9,   1,  -2,  -7,  -8,  -8, -12, -16, -14,  -5,   5,
+                -68, -50, -13,   5,   0,  -9,  -9,  -8, -13, -20, -19,  -3,  15},
+        /* Feature vec 5 : this should have valid delta 1 and delta 2. */
+        {-31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -11, -12, -12,
+                -62, -45, -11,   5,   0,  -8,  -9,  -8, -12, -19, -17,  -3,  13,
+                -27, -22, -13,  -9, -11, -12, -12, -11, -11, -13, -13, -10,  -6},
+        /* Feature vec 6. */
+        {-31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+        /* Feature vec 7. */
+        {-32,   4,  -9,  -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+        /* Feature vec 8. */
+        {-32,   4,  -9,  -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+        /* Feature vec 9. */
+        {-31,   4,  -9,  -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
+                -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+                -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10}
 };
 
 void PopulateTestWavVector(std::vector<int16_t>& vec)
@@ -97,17 +87,17 @@ void PopulateTestWavVector(std::vector<int16_t>& vec)
 
 TEST_CASE("Preprocessing calculation INT8")
 {
-
     /* Constants. */
-    const uint32_t  windowLen       = 512;
-    const uint32_t  windowStride    = 160;
-    int             dimArray[]      = {3, 1, numMfccFeatures * 3, numMfccVectors};
-    const float     quantScale      = 0.1410219967365265;
-    const int       quantOffset     = -11;
+    const uint32_t  mfccWindowLen      = 512;
+    const uint32_t  mfccWindowStride   = 160;
+    int             dimArray[]         = {3, 1, numMfccFeatures * 3, numMfccVectors};
+    const float     quantScale         = 0.1410219967365265;
+    const int       quantOffset        = -11;
 
     /* Test wav memory. */
-    std::vector <int16_t> testWav((windowStride * numMfccVectors) +
-                                  (windowLen - windowStride));
+    std::vector<int16_t> testWav((mfccWindowStride * numMfccVectors) +
+                                 (mfccWindowLen - mfccWindowStride)
+    );
 
     /* Populate with dummy input. */
     PopulateTestWavVector(testWav);
@@ -117,20 +107,20 @@ TEST_CASE("Preprocessing calculation INT8")
 
     /* Initialise dimensions and the test tensor. */
     TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
-    TfLiteTensor tensor = tflite::testing::CreateQuantizedTensor(
-        tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
+    TfLiteTensor inputTensor = tflite::testing::CreateQuantizedTensor(
+            tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
 
     /* Initialise pre-processing module. */
-    arm::app::audio::asr::Preprocess prep{
-        numMfccFeatures, windowLen, windowStride, numMfccVectors};
+    arm::app::AsrPreProcess prep{&inputTensor,
+                                 numMfccFeatures, numMfccVectors, mfccWindowLen, mfccWindowStride};
 
     /* Invoke pre-processing. */
-    REQUIRE(prep.Invoke(testWav.data(), testWav.size(), &tensor));
+    REQUIRE(prep.DoPreProcess(testWav.data(), testWav.size()));
 
     /* Wrap the tensor with a std::vector for ease. */
-    int8_t * tensorData = tflite::GetTensorData<int8_t>(&tensor);
+    auto* tensorData = tflite::GetTensorData<int8_t>(&inputTensor);
     std::vector <int8_t> vecResults =
-        std::vector<int8_t>(tensorData, tensorData + tensor.bytes);
+            std::vector<int8_t>(tensorData, tensorData + inputTensor.bytes);
 
     /* Check sizes. */
     REQUIRE(vecResults.size() == sizeof(expectedResult));
diff --git a/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp b/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp
index e28a6da..ca5aab1 100644
--- a/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp
+++ b/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "RNNoiseProcess.hpp"
+#include "RNNoiseFeatureProcessor.hpp"
 #include <catch.hpp>
 #include <limits>
 
@@ -208,7 +208,7 @@ TEST_CASE("RNNoise preprocessing calculation test",  "[RNNoise]")
 {
     SECTION("FP32")
     {
-        arm::app::rnn::RNNoiseProcess rnnoiseProcessor;
+        arm::app::rnn::RNNoiseFeatureProcessor rnnoiseProcessor;
         arm::app::rnn::FrameFeatures features;
 
         rnnoiseProcessor.PreprocessFrame(testWav0.data(), testWav0.size(), features);
@@ -223,7 +223,7 @@ TEST_CASE("RNNoise preprocessing calculation test",  "[RNNoise]")
 
 TEST_CASE("RNNoise postprocessing test", "[RNNoise]")
 {
-    arm::app::rnn::RNNoiseProcess rnnoiseProcessor;
+    arm::app::rnn::RNNoiseFeatureProcessor rnnoiseProcessor;
     arm::app::rnn::FrameFeatures p;
     rnnoiseProcessor.PreprocessFrame(testWav0.data(), testWav0.size(), p);
     std::vector<float> denoised(testWav0.size());