MLECO-3183: Refactoring application sources

Platform agnostic application sources are moved into application
api module with their own independent CMake projects.

Changes for MLECO-3080 also included - they create CMake projects
individial API's (again, platform agnostic) that dependent on the
common logic. The API for KWS_API "joint" API has been removed and
now the use case relies on individual KWS, and ASR API libraries.

Change-Id: I1f7748dc767abb3904634a04e0991b74ac7b756d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>

4 files changed, 25 insertions, 1105 deletions

diff --git a/source/use_case/noise_reduction/src/MainLoop.cc b/source/use_case/noise_reduction/src/MainLoop.cc
index fd72127..4c74a48 100644
--- a/source/use_case/noise_reduction/src/MainLoop.cc
+++ b/source/use_case/noise_reduction/src/MainLoop.cc
@@ -18,7 +18,17 @@
 #include "UseCaseCommonUtils.hpp"   /* Utils functions. */
 #include "RNNoiseModel.hpp"         /* Model class for running inference. */
 #include "InputFiles.hpp"           /* For input audio clips. */
-#include "log_macros.h"
+#include "log_macros.h"             /* Logging functions */
+#include "BufAttributes.hpp"        /* Buffer attributes to be applied */
+
+namespace arm {
+    namespace app {
+        static uint8_t  tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+    } /* namespace app */
+} /* namespace arm */
+
+extern uint8_t* GetModelPointer();
+extern size_t GetModelLen();
 
 enum opcodes
 {
@@ -62,10 +72,22 @@ void main_loop()
     constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false;
 
     /* Load the model. */
-    if (!model.Init()) {
+    if (!model.Init(arm::app::tensorArena,
+                    sizeof(arm::app::tensorArena),
+                    GetModelPointer(),
+                    GetModelLen())) {
         printf_err("Failed to initialise model\n");
         return;
     }
+
+#if !defined(ARM_NPU)
+    /* If it is not a NPU build check if the model contains a NPU operator */
+    if (model.ContainsEthosUOperator()) {
+        printf_err("No driver support for Ethos-U operator found in the model.\n");
+        return;
+    }
+#endif /* ARM_NPU */
+
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
@@ -124,4 +146,4 @@ void main_loop()
         }
     } while (executionSuccessful && bUseMenu);
     info("Main loop terminated.\n");
-}
\ No newline at end of file
+}
diff --git a/source/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc b/source/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc
deleted file mode 100644
index 036894c..0000000
--- a/source/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc
+++ /dev/null
@@ -1,892 +0,0 @@
-/*
- * 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");
- * 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 "RNNoiseFeatureProcessor.hpp"
-#include "log_macros.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstring>
-
-namespace arm {
-namespace app {
-namespace rnn {
-
-#define VERIFY(x)                                   \
-do {                                                \
-    if (!(x)) {                                     \
-        printf_err("Assert failed:" #x "\n");       \
-        exit(1);                                    \
-    }                                               \
-} while(0)
-
-RNNoiseFeatureProcessor::RNNoiseFeatureProcessor() :
-        m_halfWindow(FRAME_SIZE, 0),
-        m_dctTable(NB_BANDS * NB_BANDS),
-        m_analysisMem(FRAME_SIZE, 0),
-        m_cepstralMem(CEPS_MEM, vec1D32F(NB_BANDS, 0)),
-        m_memId{0},
-        m_synthesisMem(FRAME_SIZE, 0),
-        m_pitchBuf(PITCH_BUF_SIZE, 0),
-        m_lastGain{0.0},
-        m_lastPeriod{0},
-        m_memHpX{},
-        m_lastGVec(NB_BANDS, 0)
-{
-    constexpr uint32_t numFFt = 2 * FRAME_SIZE;
-    static_assert(numFFt != 0, "Num FFT can't be 0");
-
-    math::MathUtils::FftInitF32(numFFt, this->m_fftInstReal, FftType::real);
-    math::MathUtils::FftInitF32(numFFt, this->m_fftInstCmplx, FftType::complex);
-    this->InitTables();
-}
-
-void RNNoiseFeatureProcessor::PreprocessFrame(const float*   audioData,
-                                              const size_t   audioLen,
-                                              FrameFeatures& features)
-{
-    /* Note audioWindow is modified in place */
-    const arrHp aHp {-1.99599, 0.99600 };
-    const arrHp bHp {-2.00000, 1.00000 };
-
-    vec1D32F audioWindow{audioData, audioData + audioLen};
-
-    this->BiQuad(bHp, aHp, this->m_memHpX, audioWindow);
-    this->ComputeFrameFeatures(audioWindow, features);
-}
-
-void RNNoiseFeatureProcessor::PostProcessFrame(vec1D32F& modelOutput, FrameFeatures& features, vec1D32F& outFrame)
-{
-    std::vector<float> outputBands = modelOutput;
-    std::vector<float> gain(FREQ_SIZE, 0);
-
-    if (!features.m_silence) {
-        PitchFilter(features, outputBands);
-        for (size_t i = 0; i < NB_BANDS; i++) {
-            float alpha = .6f;
-            outputBands[i] = std::max(outputBands[i], alpha * m_lastGVec[i]);
-            m_lastGVec[i] = outputBands[i];
-        }
-        InterpBandGain(gain, outputBands);
-        for (size_t i = 0; i < FREQ_SIZE; i++) {
-            features.m_fftX[2 * i] *= gain[i];  /* Real. */
-            features.m_fftX[2 * i + 1] *= gain[i];  /*imaginary. */
-
-        }
-
-    }
-
-    FrameSynthesis(outFrame, features.m_fftX);
-}
-
-void RNNoiseFeatureProcessor::InitTables()
-{
-    constexpr float pi = M_PI;
-    constexpr float halfPi = M_PI / 2;
-    constexpr float halfPiOverFrameSz = halfPi/FRAME_SIZE;
-
-    for (uint32_t i = 0; i < FRAME_SIZE; i++) {
-        const float sinVal = math::MathUtils::SineF32(halfPiOverFrameSz * (i + 0.5f));
-        m_halfWindow[i] = math::MathUtils::SineF32(halfPi * sinVal * sinVal);
-    }
-
-    for (uint32_t i = 0; i < NB_BANDS; i++) {
-        for (uint32_t j = 0; j < NB_BANDS; j++) {
-            m_dctTable[i * NB_BANDS + j] = math::MathUtils::CosineF32((i + 0.5f) * j * pi / NB_BANDS);
-        }
-        m_dctTable[i * NB_BANDS] *= math::MathUtils::SqrtF32(0.5f);
-    }
-}
-
-void RNNoiseFeatureProcessor::BiQuad(
-        const arrHp& bHp,
-        const arrHp& aHp,
-        arrHp& memHpX,
-        vec1D32F& audioWindow)
-{
-    for (float& audioElement : audioWindow) {
-        const auto xi = audioElement;
-        const auto yi = audioElement + memHpX[0];
-        memHpX[0] = memHpX[1] + (bHp[0] * xi - aHp[0] * yi);
-        memHpX[1] = (bHp[1] * xi - aHp[1] * yi);
-        audioElement = yi;
-    }
-}
-
-void RNNoiseFeatureProcessor::ComputeFrameFeatures(vec1D32F& audioWindow,
-                                                   FrameFeatures& features)
-{
-    this->FrameAnalysis(audioWindow,
-                        features.m_fftX,
-                        features.m_Ex,
-                        this->m_analysisMem);
-
-    float energy = 0.0;
-
-    vec1D32F Ly(NB_BANDS, 0);
-    vec1D32F p(WINDOW_SIZE, 0);
-    vec1D32F pitchBuf(PITCH_BUF_SIZE >> 1, 0);
-
-    VERIFY(PITCH_BUF_SIZE >= this->m_pitchBuf.size());
-    std::copy_n(this->m_pitchBuf.begin() + FRAME_SIZE,
-                PITCH_BUF_SIZE - FRAME_SIZE,
-                this->m_pitchBuf.begin());
-
-    VERIFY(FRAME_SIZE <= audioWindow.size() && PITCH_BUF_SIZE > FRAME_SIZE);
-    std::copy_n(audioWindow.begin(),
-                FRAME_SIZE,
-                this->m_pitchBuf.begin() + PITCH_BUF_SIZE - FRAME_SIZE);
-
-    this->PitchDownsample(pitchBuf, PITCH_BUF_SIZE);
-
-    VERIFY(pitchBuf.size() > PITCH_MAX_PERIOD/2);
-    vec1D32F xLp(pitchBuf.size() - PITCH_MAX_PERIOD/2, 0);
-    std::copy_n(pitchBuf.begin() + PITCH_MAX_PERIOD/2, xLp.size(), xLp.begin());
-
-    int pitchIdx = this->PitchSearch(xLp, pitchBuf,
-            PITCH_FRAME_SIZE, (PITCH_MAX_PERIOD - (3*PITCH_MIN_PERIOD)));
-
-    pitchIdx = this->RemoveDoubling(
-                pitchBuf,
-                PITCH_MAX_PERIOD,
-                PITCH_MIN_PERIOD,
-                PITCH_FRAME_SIZE,
-                PITCH_MAX_PERIOD - pitchIdx);
-
-    size_t stIdx = PITCH_BUF_SIZE - WINDOW_SIZE - pitchIdx;
-    VERIFY((static_cast<int>(PITCH_BUF_SIZE) - static_cast<int>(WINDOW_SIZE) - pitchIdx) >= 0);
-    std::copy_n(this->m_pitchBuf.begin() + stIdx, WINDOW_SIZE, p.begin());
-
-    this->ApplyWindow(p);
-    this->ForwardTransform(p, features.m_fftP);
-    this->ComputeBandEnergy(features.m_fftP, features.m_Ep);
-    this->ComputeBandCorr(features.m_fftX, features.m_fftP, features.m_Exp);
-
-    for (uint32_t i = 0 ; i < NB_BANDS; ++i) {
-        features.m_Exp[i] /= math::MathUtils::SqrtF32(
-            0.001f + features.m_Ex[i] * features.m_Ep[i]);
-    }
-
-    vec1D32F dctVec(NB_BANDS, 0);
-    this->DCT(features.m_Exp, dctVec);
-
-    features.m_featuresVec = vec1D32F (NB_FEATURES, 0);
-    for (uint32_t i = 0; i < NB_DELTA_CEPS; ++i) {
-        features.m_featuresVec[NB_BANDS + 2*NB_DELTA_CEPS + i] = dctVec[i];
-    }
-
-    features.m_featuresVec[NB_BANDS + 2*NB_DELTA_CEPS] -= 1.3;
-    features.m_featuresVec[NB_BANDS + 2*NB_DELTA_CEPS + 1] -= 0.9;
-    features.m_featuresVec[NB_BANDS + 3*NB_DELTA_CEPS] = 0.01 * (static_cast<int>(pitchIdx) - 300);
-
-    float logMax = -2.f;
-    float follow = -2.f;
-    for (uint32_t i = 0; i < NB_BANDS; ++i) {
-        Ly[i] = log10f(1e-2f + features.m_Ex[i]);
-        Ly[i] = std::max<float>(logMax - 7, std::max<float>(follow - 1.5, Ly[i]));
-        logMax = std::max<float>(logMax, Ly[i]);
-        follow = std::max<float>(follow - 1.5, Ly[i]);
-        energy += features.m_Ex[i];
-    }
-
-    /* If there's no audio avoid messing up the state. */
-    features.m_silence = true;
-    if (energy < 0.04) {
-        return;
-    } else {
-        features.m_silence = false;
-    }
-
-    this->DCT(Ly, features.m_featuresVec);
-    features.m_featuresVec[0] -= 12.0;
-    features.m_featuresVec[1] -= 4.0;
-
-    VERIFY(CEPS_MEM > 2);
-    uint32_t stIdx1 = this->m_memId < 1 ? CEPS_MEM + this->m_memId - 1 : this->m_memId - 1;
-    uint32_t stIdx2 = this->m_memId < 2 ? CEPS_MEM + this->m_memId - 2 : this->m_memId - 2;
-    VERIFY(stIdx1 < this->m_cepstralMem.size());
-    VERIFY(stIdx2 < this->m_cepstralMem.size());
-    auto ceps1 = this->m_cepstralMem[stIdx1];
-    auto ceps2 = this->m_cepstralMem[stIdx2];
-
-    /* Ceps 0 */
-    for (uint32_t i = 0; i < NB_BANDS; ++i) {
-        this->m_cepstralMem[this->m_memId][i] = features.m_featuresVec[i];
-    }
-
-    for (uint32_t i = 0; i < NB_DELTA_CEPS; ++i) {
-        features.m_featuresVec[i] = this->m_cepstralMem[this->m_memId][i] + ceps1[i] + ceps2[i];
-        features.m_featuresVec[NB_BANDS + i] = this->m_cepstralMem[this->m_memId][i] - ceps2[i];
-        features.m_featuresVec[NB_BANDS + NB_DELTA_CEPS + i] =
-                this->m_cepstralMem[this->m_memId][i] - 2 * ceps1[i] + ceps2[i];
-    }
-
-    /* Spectral variability features. */
-    this->m_memId += 1;
-    if (this->m_memId == CEPS_MEM) {
-        this->m_memId = 0;
-    }
-
-    float specVariability = 0.f;
-
-    VERIFY(this->m_cepstralMem.size() >= CEPS_MEM);
-    for (size_t i = 0; i < CEPS_MEM; ++i) {
-        float minDist = 1e15;
-        for (size_t j = 0; j < CEPS_MEM; ++j) {
-            float dist = 0.f;
-            for (size_t k = 0; k < NB_BANDS; ++k) {
-                VERIFY(this->m_cepstralMem[i].size() >= NB_BANDS);
-                auto tmp = this->m_cepstralMem[i][k] - this->m_cepstralMem[j][k];
-                dist += tmp * tmp;
-            }
-
-            if (j != i) {
-                minDist = std::min<float>(minDist, dist);
-            }
-        }
-        specVariability += minDist;
-    }
-
-    VERIFY(features.m_featuresVec.size() >= NB_BANDS + 3 * NB_DELTA_CEPS + 1);
-    features.m_featuresVec[NB_BANDS + 3 * NB_DELTA_CEPS + 1] = specVariability / CEPS_MEM - 2.1;
-}
-
-void RNNoiseFeatureProcessor::FrameAnalysis(
-    const vec1D32F& audioWindow,
-    vec1D32F& fft,
-    vec1D32F& energy,
-    vec1D32F& analysisMem)
-{
-    vec1D32F x(WINDOW_SIZE, 0);
-
-    /* Move old audio down and populate end with latest audio window. */
-    VERIFY(x.size() >= FRAME_SIZE && analysisMem.size() >= FRAME_SIZE);
-    VERIFY(audioWindow.size() >= FRAME_SIZE);
-
-    std::copy_n(analysisMem.begin(), FRAME_SIZE, x.begin());
-    std::copy_n(audioWindow.begin(), x.size() - FRAME_SIZE, x.begin() + FRAME_SIZE);
-    std::copy_n(audioWindow.begin(), FRAME_SIZE, analysisMem.begin());
-
-    this->ApplyWindow(x);
-
-    /* Calculate FFT. */
-    ForwardTransform(x, fft);
-
-    /* Compute band energy. */
-    ComputeBandEnergy(fft, energy);
-}
-
-void RNNoiseFeatureProcessor::ApplyWindow(vec1D32F& x)
-{
-    if (WINDOW_SIZE != x.size()) {
-        printf_err("Invalid size for vector to be windowed\n");
-        return;
-    }
-
-    VERIFY(this->m_halfWindow.size() >= FRAME_SIZE);
-
-    /* Multiply input by sinusoidal function. */
-    for (size_t i = 0; i < FRAME_SIZE; i++) {
-        x[i] *= this->m_halfWindow[i];
-        x[WINDOW_SIZE - 1 - i] *= this->m_halfWindow[i];
-    }
-}
-
-void RNNoiseFeatureProcessor::ForwardTransform(
-    vec1D32F& x,
-    vec1D32F& fft)
-{
-    /* The input vector can be modified by the fft function. */
-    fft.reserve(x.size() + 2);
-    fft.resize(x.size() + 2, 0);
-    math::MathUtils::FftF32(x, fft, this->m_fftInstReal);
-
-    /* Normalise. */
-    for (auto& f : fft) {
-        f /= this->m_fftInstReal.m_fftLen;
-    }
-
-    /* Place the last freq element correctly */
-    fft[fft.size()-2] = fft[1];
-    fft[1] = 0;
-
-    /* NOTE: We don't truncate out FFT vector as it already contains only the
-     * first half of the FFT's. The conjugates are not present. */
-}
-
-void RNNoiseFeatureProcessor::ComputeBandEnergy(const vec1D32F& fftX, vec1D32F& bandE)
-{
-    bandE = vec1D32F(NB_BANDS, 0);
-
-    VERIFY(this->m_eband5ms.size() >= NB_BANDS);
-    for (uint32_t i = 0; i < NB_BANDS - 1; i++) {
-        const auto bandSize = (this->m_eband5ms[i + 1] - this->m_eband5ms[i])
-                              << FRAME_SIZE_SHIFT;
-
-        for (uint32_t j = 0; j < bandSize; j++) {
-            const auto frac = static_cast<float>(j) / bandSize;
-            const auto idx = (this->m_eband5ms[i] << FRAME_SIZE_SHIFT) + j;
-
-            auto tmp = fftX[2 * idx] * fftX[2 * idx]; /* Real part */
-            tmp += fftX[2 * idx + 1] * fftX[2 * idx + 1]; /* Imaginary part */
-
-            bandE[i] += (1 - frac) * tmp;
-            bandE[i + 1] += frac * tmp;
-        }
-    }
-    bandE[0] *= 2;
-    bandE[NB_BANDS - 1] *= 2;
-}
-
-void RNNoiseFeatureProcessor::ComputeBandCorr(const vec1D32F& X, const vec1D32F& P, vec1D32F& bandC)
-{
-    bandC = vec1D32F(NB_BANDS, 0);
-    VERIFY(this->m_eband5ms.size() >= NB_BANDS);
-
-    for (uint32_t i = 0; i < NB_BANDS - 1; i++) {
-        const auto bandSize = (this->m_eband5ms[i + 1] - this->m_eband5ms[i]) << FRAME_SIZE_SHIFT;
-
-        for (uint32_t j = 0; j < bandSize; j++) {
-            const auto frac = static_cast<float>(j) / bandSize;
-            const auto idx = (this->m_eband5ms[i] << FRAME_SIZE_SHIFT) + j;
-
-            auto tmp = X[2 * idx] * P[2 * idx]; /* Real part */
-            tmp += X[2 * idx + 1] * P[2 * idx + 1]; /* Imaginary part */
-
-            bandC[i] += (1 - frac) * tmp;
-            bandC[i + 1] += frac * tmp;
-        }
-    }
-    bandC[0] *= 2;
-    bandC[NB_BANDS - 1] *= 2;
-}
-
-void RNNoiseFeatureProcessor::DCT(vec1D32F& input, vec1D32F& output)
-{
-    VERIFY(this->m_dctTable.size() >= NB_BANDS * NB_BANDS);
-    for (uint32_t i = 0; i < NB_BANDS; ++i) {
-        float sum = 0;
-
-        for (uint32_t j = 0, k = 0; j < NB_BANDS; ++j, k += NB_BANDS) {
-            sum += input[j] * this->m_dctTable[k + i];
-        }
-        output[i] = sum * math::MathUtils::SqrtF32(2.0/22);
-    }
-}
-
-void RNNoiseFeatureProcessor::PitchDownsample(vec1D32F& pitchBuf, size_t pitchBufSz) {
-    for (size_t i = 1; i < (pitchBufSz >> 1); ++i) {
-        pitchBuf[i] = 0.5 * (
-                        0.5 * (this->m_pitchBuf[2 * i - 1] + this->m_pitchBuf[2 * i + 1])
-                            + this->m_pitchBuf[2 * i]);
-    }
-
-    pitchBuf[0] = 0.5*(0.5*(this->m_pitchBuf[1]) + this->m_pitchBuf[0]);
-
-    vec1D32F ac(5, 0);
-    size_t numLags = 4;
-
-    this->AutoCorr(pitchBuf, ac, numLags, pitchBufSz >> 1);
-
-    /* Noise floor -40db */
-    ac[0] *= 1.0001;
-
-    /* Lag windowing. */
-    for (size_t i = 1; i < numLags + 1; ++i) {
-        ac[i] -= ac[i] * (0.008 * i) * (0.008 * i);
-    }
-
-    vec1D32F lpc(numLags, 0);
-    this->LPC(ac, numLags, lpc);
-
-    float tmp = 1.0;
-    for (size_t i = 0; i < numLags; ++i) {
-        tmp = 0.9f * tmp;
-        lpc[i] = lpc[i] * tmp;
-    }
-
-    vec1D32F lpc2(numLags + 1, 0);
-    float c1 = 0.8;
-
-    /* Add a zero. */
-    lpc2[0] = lpc[0] + 0.8;
-    lpc2[1] = lpc[1] + (c1 * lpc[0]);
-    lpc2[2] = lpc[2] + (c1 * lpc[1]);
-    lpc2[3] = lpc[3] + (c1 * lpc[2]);
-    lpc2[4] = (c1 * lpc[3]);
-
-    this->Fir5(lpc2, pitchBufSz >> 1, pitchBuf);
-}
-
-int RNNoiseFeatureProcessor::PitchSearch(vec1D32F& xLp, vec1D32F& y, uint32_t len, uint32_t maxPitch) {
-    uint32_t lag = len + maxPitch;
-    vec1D32F xLp4(len >> 2, 0);
-    vec1D32F yLp4(lag >> 2, 0);
-    vec1D32F xCorr(maxPitch >> 1, 0);
-
-    /* Downsample by 2 again. */
-    for (size_t j = 0; j < (len >> 2); ++j) {
-        xLp4[j] = xLp[2*j];
-    }
-    for (size_t j = 0; j < (lag >> 2); ++j) {
-        yLp4[j] = y[2*j];
-    }
-
-    this->PitchXCorr(xLp4, yLp4, xCorr, len >> 2, maxPitch >> 2);
-
-    /* Coarse search with 4x decimation. */
-    arrHp bestPitch = this->FindBestPitch(xCorr, yLp4, len >> 2, maxPitch >> 2);
-
-    /* Finer search with 2x decimation. */
-    const int maxIdx = (maxPitch >> 1);
-    for (int i = 0; i < maxIdx; ++i) {
-        xCorr[i] = 0;
-        if (std::abs(i - 2*bestPitch[0]) > 2 and std::abs(i - 2*bestPitch[1]) > 2) {
-            continue;
-        }
-        float sum = 0;
-        for (size_t j = 0; j < len >> 1; ++j) {
-            sum += xLp[j] * y[i+j];
-        }
-
-        xCorr[i] = std::max(-1.0f, sum);
-    }
-
-    bestPitch = this->FindBestPitch(xCorr, y, len >> 1, maxPitch >> 1);
-
-    int offset;
-    /* Refine by pseudo-interpolation. */
-    if ( 0 < bestPitch[0] && bestPitch[0] < ((maxPitch >> 1) - 1)) {
-        float a = xCorr[bestPitch[0] - 1];
-        float b = xCorr[bestPitch[0]];
-        float c = xCorr[bestPitch[0] + 1];
-
-        if ( (c-a) > 0.7*(b-a) ) {
-            offset = 1;
-        } else if ( (a-c) > 0.7*(b-c) ) {
-            offset = -1;
-        } else {
-            offset = 0;
-        }
-    } else {
-        offset = 0;
-    }
-
-    return 2*bestPitch[0] - offset;
-}
-
-arrHp RNNoiseFeatureProcessor::FindBestPitch(vec1D32F& xCorr, vec1D32F& y, uint32_t len, uint32_t maxPitch)
-{
-    float Syy = 1;
-    arrHp bestNum {-1, -1};
-    arrHp bestDen {0, 0};
-    arrHp bestPitch {0, 1};
-
-    for (size_t j = 0; j < len; ++j) {
-        Syy += (y[j] * y[j]);
-    }
-
-    for (size_t i = 0; i < maxPitch; ++i ) {
-        if (xCorr[i] > 0) {
-            float xCorr16 = xCorr[i] * 1e-12f;  /* Avoid problems when squaring. */
-
-            float num = xCorr16 * xCorr16;
-            if (num*bestDen[1] > bestNum[1]*Syy) {
-                if (num*bestDen[0] > bestNum[0]*Syy) {
-                    bestNum[1] = bestNum[0];
-                    bestDen[1] = bestDen[0];
-                    bestPitch[1] = bestPitch[0];
-                    bestNum[0] = num;
-                    bestDen[0] = Syy;
-                    bestPitch[0] = i;
-                } else {
-                    bestNum[1] = num;
-                    bestDen[1] = Syy;
-                    bestPitch[1] = i;
-                }
-            }
-        }
-
-        Syy += (y[i+len]*y[i+len]) - (y[i]*y[i]);
-        Syy = std::max(1.0f, Syy);
-    }
-
-    return bestPitch;
-}
-
-int RNNoiseFeatureProcessor::RemoveDoubling(
-    vec1D32F& pitchBuf,
-    uint32_t maxPeriod,
-    uint32_t minPeriod,
-    uint32_t frameSize,
-    size_t pitchIdx0_)
-{
-    constexpr std::array<size_t, 16> secondCheck {0, 0, 3, 2, 3, 2, 5, 2, 3, 2, 3, 2, 5, 2, 3, 2};
-    uint32_t minPeriod0 = minPeriod;
-    float lastPeriod = static_cast<float>(this->m_lastPeriod)/2;
-    float lastGain = static_cast<float>(this->m_lastGain);
-
-    maxPeriod /= 2;
-    minPeriod /= 2;
-    pitchIdx0_ /= 2;
-    frameSize /= 2;
-    uint32_t xStart = maxPeriod;
-
-    if (pitchIdx0_ >= maxPeriod) {
-        pitchIdx0_ = maxPeriod - 1;
-    }
-
-    size_t pitchIdx  = pitchIdx0_;
-    const size_t pitchIdx0 = pitchIdx0_;
-
-    float xx = 0;
-    for ( size_t i = xStart; i < xStart+frameSize; ++i) {
-        xx += (pitchBuf[i] * pitchBuf[i]);
-    }
-
-    float xy = 0;
-    for ( size_t i = xStart; i < xStart+frameSize; ++i) {
-        xy += (pitchBuf[i] * pitchBuf[i-pitchIdx0]);
-    }
-
-    vec1D32F yyLookup (maxPeriod+1, 0);
-    yyLookup[0] = xx;
-    float yy = xx;
-
-    for ( size_t i = 1; i < yyLookup.size(); ++i) {
-        yy = yy + (pitchBuf[xStart-i] * pitchBuf[xStart-i]) -
-                (pitchBuf[xStart+frameSize-i] * pitchBuf[xStart+frameSize-i]);
-        yyLookup[i] = std::max(0.0f, yy);
-    }
-
-    yy = yyLookup[pitchIdx0];
-    float bestXy = xy;
-    float bestYy = yy;
-
-    float g = this->ComputePitchGain(xy, xx, yy);
-    float g0 = g;
-
-    /* Look for any pitch at pitchIndex/k. */
-    for ( size_t k = 2; k < 16; ++k) {
-        size_t pitchIdx1 = (2*pitchIdx0+k) / (2*k);
-        if (pitchIdx1 < minPeriod) {
-            break;
-        }
-
-        size_t pitchIdx1b;
-        /* Look for another strong correlation at T1b. */
-        if (k == 2) {
-            if ((pitchIdx1 + pitchIdx0) > maxPeriod) {
-                pitchIdx1b = pitchIdx0;
-            } else {
-                pitchIdx1b = pitchIdx0 + pitchIdx1;
-            }
-        } else {
-            pitchIdx1b = (2*(secondCheck[k])*pitchIdx0 + k) / (2*k);
-        }
-
-        xy = 0;
-        for ( size_t i = xStart; i < xStart+frameSize; ++i) {
-            xy += (pitchBuf[i] * pitchBuf[i-pitchIdx1]);
-        }
-
-        float xy2 = 0;
-        for ( size_t i = xStart; i < xStart+frameSize; ++i) {
-            xy2 += (pitchBuf[i] * pitchBuf[i-pitchIdx1b]);
-        }
-        xy = 0.5f * (xy + xy2);
-        VERIFY(pitchIdx1b < maxPeriod+1);
-        yy = 0.5f * (yyLookup[pitchIdx1] + yyLookup[pitchIdx1b]);
-
-        float g1 = this->ComputePitchGain(xy, xx, yy);
-
-        float cont;
-        if (std::abs(pitchIdx1-lastPeriod) <= 1) {
-            cont = lastGain;
-        } else if (std::abs(pitchIdx1-lastPeriod) <= 2 and 5*k*k < pitchIdx0) {
-            cont = 0.5f*lastGain;
-        } else {
-            cont = 0.0f;
-        }
-
-        float thresh = std::max(0.3, 0.7*g0-cont);
-
-        /* Bias against very high pitch (very short period) to avoid false-positives
-         * due to short-term correlation */
-        if (pitchIdx1 < 3*minPeriod) {
-            thresh = std::max(0.4, 0.85*g0-cont);
-        } else if (pitchIdx1 < 2*minPeriod) {
-            thresh = std::max(0.5, 0.9*g0-cont);
-        }
-        if (g1 > thresh) {
-            bestXy = xy;
-            bestYy = yy;
-            pitchIdx = pitchIdx1;
-            g = g1;
-        }
-    }
-
-    bestXy = std::max(0.0f, bestXy);
-    float pg;
-    if (bestYy <= bestXy) {
-        pg = 1.0;
-    } else {
-        pg = bestXy/(bestYy+1);
-    }
-
-    std::array<float, 3> xCorr {0};
-    for ( size_t k = 0; k < 3; ++k ) {
-        for ( size_t i = xStart; i < xStart+frameSize; ++i) {
-            xCorr[k] += (pitchBuf[i] * pitchBuf[i-(pitchIdx+k-1)]);
-        }
-    }
-
-    size_t offset;
-    if ((xCorr[2]-xCorr[0]) > 0.7*(xCorr[1]-xCorr[0])) {
-        offset = 1;
-    } else if ((xCorr[0]-xCorr[2]) > 0.7*(xCorr[1]-xCorr[2])) {
-        offset = -1;
-    } else {
-        offset = 0;
-    }
-
-    if (pg > g) {
-        pg = g;
-    }
-
-    pitchIdx0_ = 2*pitchIdx + offset;
-
-    if (pitchIdx0_ < minPeriod0) {
-        pitchIdx0_ = minPeriod0;
-    }
-
-    this->m_lastPeriod = pitchIdx0_;
-    this->m_lastGain = pg;
-
-    return this->m_lastPeriod;
-}
-
-float RNNoiseFeatureProcessor::ComputePitchGain(float xy, float xx, float yy)
-{
-    return xy / math::MathUtils::SqrtF32(1+xx*yy);
-}
-
-void RNNoiseFeatureProcessor::AutoCorr(
-    const vec1D32F& x,
-    vec1D32F& ac,
-    size_t lag,
-    size_t n)
-{
-    if (n < lag) {
-        printf_err("Invalid parameters for AutoCorr\n");
-        return;
-    }
-
-    auto fastN = n - lag;
-
-    /* Auto-correlation - can be done by PlatformMath functions */
-    this->PitchXCorr(x, x, ac, fastN, lag + 1);
-
-    /* Modify auto-correlation by summing with auto-correlation for different lags. */
-    for (size_t k = 0; k < lag + 1; k++) {
-        float d = 0;
-        for (size_t i = k + fastN; i < n; i++) {
-            d += x[i] * x[i - k];
-        }
-        ac[k] += d;
-    }
-}
-
-
-void RNNoiseFeatureProcessor::PitchXCorr(
-    const vec1D32F& x,
-    const vec1D32F& y,
-    vec1D32F& xCorr,
-    size_t len,
-    size_t maxPitch)
-{
-    for (size_t i = 0; i < maxPitch; i++) {
-        float sum = 0;
-        for (size_t j = 0; j < len; j++) {
-            sum += x[j] * y[i + j];
-        }
-        xCorr[i] = sum;
-    }
-}
-
-/* Linear predictor coefficients */
-void RNNoiseFeatureProcessor::LPC(
-    const vec1D32F& correlation,
-    int32_t p,
-    vec1D32F& lpc)
-{
-    auto error = correlation[0];
-
-    if (error != 0) {
-        for (int i = 0; i < p; i++) {
-
-            /* Sum up this iteration's reflection coefficient */
-            float rr = 0;
-            for (int j = 0; j < i; j++) {
-                rr += lpc[j] * correlation[i - j];
-            }
-
-            rr += correlation[i + 1];
-            auto r = -rr / error;
-
-            /* Update LP coefficients and total error */
-            lpc[i] = r;
-            for (int j = 0; j < ((i + 1) >> 1); j++) {
-                auto tmp1 = lpc[j];
-                auto tmp2 = lpc[i - 1 - j];
-                lpc[j] = tmp1 + (r * tmp2);
-                lpc[i - 1 - j] = tmp2 + (r * tmp1);
-            }
-
-            error = error - (r * r * error);
-
-            /* Bail out once we get 30dB gain */
-            if (error < (0.001 * correlation[0])) {
-                break;
-            }
-        }
-    }
-}
-
-void RNNoiseFeatureProcessor::Fir5(
-    const vec1D32F &num,
-    uint32_t N,
-    vec1D32F &x)
-{
-    auto num0 = num[0];
-    auto num1 = num[1];
-    auto num2 = num[2];
-    auto num3 = num[3];
-    auto num4 = num[4];
-    auto mem0 = 0;
-    auto mem1 = 0;
-    auto mem2 = 0;
-    auto mem3 = 0;
-    auto mem4 = 0;
-    for (uint32_t i = 0; i < N; i++)
-    {
-        auto sum_ = x[i] +  (num0 * mem0) + (num1 * mem1) +
-                    (num2 * mem2) + (num3 * mem3) + (num4 * mem4);
-        mem4 = mem3;
-        mem3 = mem2;
-        mem2 = mem1;
-        mem1 = mem0;
-        mem0 = x[i];
-        x[i] = sum_;
-    }
-}
-
-void RNNoiseFeatureProcessor::PitchFilter(FrameFeatures &features, vec1D32F &gain) {
-    std::vector<float> r(NB_BANDS, 0);
-    std::vector<float> rf(FREQ_SIZE, 0);
-    std::vector<float> newE(NB_BANDS);
-
-    for (size_t i = 0; i < NB_BANDS; i++) {
-        if (features.m_Exp[i] > gain[i]) {
-            r[i] = 1;
-        } else {
-
-
-            r[i] = std::pow(features.m_Exp[i], 2) * (1 - std::pow(gain[i], 2)) /
-                   (.001 + std::pow(gain[i], 2) * (1 - std::pow(features.m_Exp[i], 2)));
-        }
-
-
-        r[i] = math::MathUtils::SqrtF32(std::min(1.0f, std::max(0.0f, r[i])));
-        r[i] *= math::MathUtils::SqrtF32(features.m_Ex[i] / (1e-8f + features.m_Ep[i]));
-    }
-
-    InterpBandGain(rf, r);
-    for (size_t i = 0; i < FREQ_SIZE - 1; i++) {
-        features.m_fftX[2 * i] += rf[i] * features.m_fftP[2 * i];  /* Real. */
-        features.m_fftX[2 * i + 1] += rf[i] * features.m_fftP[2 * i + 1];  /* Imaginary. */
-
-    }
-    ComputeBandEnergy(features.m_fftX, newE);
-    std::vector<float> norm(NB_BANDS);
-    std::vector<float> normf(FRAME_SIZE, 0);
-    for (size_t i = 0; i < NB_BANDS; i++) {
-        norm[i] = math::MathUtils::SqrtF32(features.m_Ex[i] / (1e-8f + newE[i]));
-    }
-
-    InterpBandGain(normf, norm);
-    for (size_t i = 0; i < FREQ_SIZE - 1; i++) {
-        features.m_fftX[2 * i] *= normf[i];  /* Real. */
-        features.m_fftX[2 * i + 1] *= normf[i];  /* Imaginary. */
-
-    }
-}
-
-void RNNoiseFeatureProcessor::FrameSynthesis(vec1D32F& outFrame, vec1D32F& fftY) {
-    std::vector<float> x(WINDOW_SIZE, 0);
-    InverseTransform(x, fftY);
-    ApplyWindow(x);
-    for (size_t i = 0; i < FRAME_SIZE; i++) {
-        outFrame[i] = x[i] + m_synthesisMem[i];
-    }
-    memcpy((m_synthesisMem.data()), &x[FRAME_SIZE], FRAME_SIZE*sizeof(float));
-}
-
-void RNNoiseFeatureProcessor::InterpBandGain(vec1D32F& g, vec1D32F& bandE) {
-    for (size_t i = 0; i < NB_BANDS - 1; i++) {
-        int bandSize = (m_eband5ms[i + 1] - m_eband5ms[i]) << FRAME_SIZE_SHIFT;
-        for (int j = 0; j < bandSize; j++) {
-            float frac = static_cast<float>(j) / bandSize;
-            g[(m_eband5ms[i] << FRAME_SIZE_SHIFT) + j] = (1 - frac) * bandE[i] + frac * bandE[i + 1];
-        }
-    }
-}
-
-void RNNoiseFeatureProcessor::InverseTransform(vec1D32F& out, vec1D32F& fftXIn) {
-
-    std::vector<float> x(WINDOW_SIZE * 2);  /* This is complex. */
-    vec1D32F newFFT;  /* This is complex. */
-
-    size_t i;
-    for (i = 0; i < FREQ_SIZE * 2; i++) {
-        x[i] = fftXIn[i];
-    }
-    for (i = FREQ_SIZE; i < WINDOW_SIZE; i++) {
-        x[2 * i] = x[2 * (WINDOW_SIZE - i)];  /* Real. */
-        x[2 * i + 1] = -x[2 * (WINDOW_SIZE - i) + 1];  /* Imaginary. */
-    }
-
-    constexpr uint32_t numFFt = 2 * FRAME_SIZE;
-    static_assert(numFFt != 0, "numFFt cannot be 0!");
-
-    vec1D32F fftOut = vec1D32F(x.size(), 0);
-    math::MathUtils::FftF32(x,fftOut, m_fftInstCmplx);
-
-    /* Normalize. */
-    for (auto &f: fftOut) {
-        f /= numFFt;
-    }
-
-    out[0] = WINDOW_SIZE * fftOut[0];  /* Real. */
-    for (i = 1; i < WINDOW_SIZE; i++) {
-        out[i] = WINDOW_SIZE * fftOut[(WINDOW_SIZE * 2) - (2 * i)];  /* Real. */
-    }
-}
-
-
-} /* namespace rnn */
-} /* namespace app */
-} /* namspace arm */
diff --git a/source/use_case/noise_reduction/src/RNNoiseModel.cc b/source/use_case/noise_reduction/src/RNNoiseModel.cc
deleted file mode 100644
index 244fa1a..0000000
--- a/source/use_case/noise_reduction/src/RNNoiseModel.cc
+++ /dev/null
@@ -1,110 +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 "RNNoiseModel.hpp"
-#include "log_macros.h"
-
-const tflite::MicroOpResolver& arm::app::RNNoiseModel::GetOpResolver()
-{
-    return this->m_opResolver;
-}
-
-bool arm::app::RNNoiseModel::EnlistOperations()
-{
-    this->m_opResolver.AddUnpack();
-    this->m_opResolver.AddFullyConnected();
-    this->m_opResolver.AddSplit();
-    this->m_opResolver.AddSplitV();
-    this->m_opResolver.AddAdd();
-    this->m_opResolver.AddLogistic();
-    this->m_opResolver.AddMul();
-    this->m_opResolver.AddSub();
-    this->m_opResolver.AddTanh();
-    this->m_opResolver.AddPack();
-    this->m_opResolver.AddReshape();
-    this->m_opResolver.AddQuantize();
-    this->m_opResolver.AddConcatenation();
-    this->m_opResolver.AddRelu();
-
-#if defined(ARM_NPU)
-    if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
-        info("Added %s support to op resolver\n",
-            tflite::GetString_ETHOSU());
-    } else {
-        printf_err("Failed to add Arm NPU support to op resolver.");
-        return false;
-    }
-#endif /* ARM_NPU */
-    return true;
-}
-
-extern uint8_t* GetModelPointer();
-const uint8_t* arm::app::RNNoiseModel::ModelPointer()
-{
-    return GetModelPointer();
-}
-
-extern size_t GetModelLen();
-size_t arm::app::RNNoiseModel::ModelSize()
-{
-    return GetModelLen();
-}
-
-bool arm::app::RNNoiseModel::RunInference()
-{
-    return Model::RunInference();
-}
-
-void arm::app::RNNoiseModel::ResetGruState()
-{
-    for (auto& stateMapping: this->m_gruStateMap) {
-        TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.second);
-        auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
-        /* Initial value of states is 0, but this is affected by quantization zero point. */
-        auto quantParams = arm::app::GetTensorQuantParams(inputGruStateTensor);
-        memset(inputGruState, quantParams.offset, inputGruStateTensor->bytes);
-    }
-}
-
-bool arm::app::RNNoiseModel::CopyGruStates()
-{
-    std::vector<std::pair<size_t, std::vector<int8_t>>> tempOutGruStates;
-    /* Saving output states before copying them to input states to avoid output states modification in the tensor.
-     * tflu shares input and output tensors memory, thus writing to input tensor can change output tensor values. */
-    for (auto& stateMapping: this->m_gruStateMap) {
-        TfLiteTensor* outputGruStateTensor = this->GetOutputTensor(stateMapping.first);
-        std::vector<int8_t> tempOutGruState(outputGruStateTensor->bytes);
-        auto* outGruState = tflite::GetTensorData<int8_t>(outputGruStateTensor);
-        memcpy(tempOutGruState.data(), outGruState, outputGruStateTensor->bytes);
-        /* Index of the input tensor and the data to copy. */
-        tempOutGruStates.emplace_back(stateMapping.second, std::move(tempOutGruState));
-    }
-    /* Updating input GRU states with saved GRU output states. */
-    for (auto& stateMapping: tempOutGruStates) {
-        auto outputGruStateTensorData = stateMapping.second;
-        TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.first);
-        if (outputGruStateTensorData.size() != inputGruStateTensor->bytes) {
-            printf_err("Unexpected number of bytes for GRU state mapping. Input = %zuz, output = %zuz.\n",
-                       inputGruStateTensor->bytes,
-                       outputGruStateTensorData.size());
-            return false;
-        }
-        auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
-        auto* outGruState = outputGruStateTensorData.data();
-        memcpy(inputGruState, outGruState, inputGruStateTensor->bytes);
-    }
-    return true;
-}
\ No newline at end of file
diff --git a/source/use_case/noise_reduction/src/RNNoiseProcessing.cc b/source/use_case/noise_reduction/src/RNNoiseProcessing.cc
deleted file mode 100644
index f6a3ec4..0000000
--- a/source/use_case/noise_reduction/src/RNNoiseProcessing.cc
+++ /dev/null
@@ -1,100 +0,0 @@
-/*
- * Copyright (c) 2022 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 "RNNoiseProcessing.hpp"
-#include "log_macros.h"
-
-namespace arm {
-namespace app {
-
-    RNNoisePreProcess::RNNoisePreProcess(TfLiteTensor* inputTensor,
-            std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, std::shared_ptr<rnn::FrameFeatures> frameFeatures)
-    :   m_inputTensor{inputTensor},
-        m_featureProcessor{featureProcessor},
-        m_frameFeatures{frameFeatures}
-    {}
-
-    bool RNNoisePreProcess::DoPreProcess(const void* data, size_t inputSize)
-    {
-        if (data == nullptr) {
-            printf_err("Data pointer is null");
-            return false;
-        }
-
-        auto input = static_cast<const int16_t*>(data);
-        this->m_audioFrame = rnn::vec1D32F(input, input + inputSize);
-        m_featureProcessor->PreprocessFrame(this->m_audioFrame.data(), inputSize, *this->m_frameFeatures);
-
-        QuantizeAndPopulateInput(this->m_frameFeatures->m_featuresVec,
-                this->m_inputTensor->params.scale, this->m_inputTensor->params.zero_point,
-                this->m_inputTensor);
-
-        debug("Input tensor populated \n");
-
-        return true;
-    }
-
-    void RNNoisePreProcess::QuantizeAndPopulateInput(rnn::vec1D32F& inputFeatures,
-            const float quantScale, const int quantOffset,
-            TfLiteTensor* inputTensor)
-    {
-        const float minVal = std::numeric_limits<int8_t>::min();
-        const float maxVal = std::numeric_limits<int8_t>::max();
-
-        auto* inputTensorData = tflite::GetTensorData<int8_t>(inputTensor);
-
-        for (size_t i=0; i < inputFeatures.size(); ++i) {
-            float quantValue = ((inputFeatures[i] / quantScale) + quantOffset);
-            inputTensorData[i] = static_cast<int8_t>(std::min<float>(std::max<float>(quantValue, minVal), maxVal));
-        }
-    }
-
-    RNNoisePostProcess::RNNoisePostProcess(TfLiteTensor* outputTensor,
-            std::vector<int16_t>& denoisedAudioFrame,
-            std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor,
-            std::shared_ptr<rnn::FrameFeatures> frameFeatures)
-    :   m_outputTensor{outputTensor},
-        m_denoisedAudioFrame{denoisedAudioFrame},
-        m_featureProcessor{featureProcessor},
-        m_frameFeatures{frameFeatures}
-        {
-            this->m_denoisedAudioFrameFloat.reserve(denoisedAudioFrame.size());
-            this->m_modelOutputFloat.resize(outputTensor->bytes);
-        }
-
-    bool RNNoisePostProcess::DoPostProcess()
-    {
-        const auto* outputData = tflite::GetTensorData<int8_t>(this->m_outputTensor);
-        auto outputQuantParams = GetTensorQuantParams(this->m_outputTensor);
-
-        for (size_t i = 0; i < this->m_outputTensor->bytes; ++i) {
-            this->m_modelOutputFloat[i] = (static_cast<float>(outputData[i]) - outputQuantParams.offset)
-                                  * outputQuantParams.scale;
-        }
-
-        this->m_featureProcessor->PostProcessFrame(this->m_modelOutputFloat,
-                *this->m_frameFeatures, this->m_denoisedAudioFrameFloat);
-
-        for (size_t i = 0; i < this->m_denoisedAudioFrame.size(); ++i) {
-            this->m_denoisedAudioFrame[i] = static_cast<int16_t>(
-                    std::roundf(this->m_denoisedAudioFrameFloat[i]));
-        }
-
-        return true;
-    }
-
-} /* namespace app */
-} /* namespace arm */
\ No newline at end of file