From aa4bcb14d0cbee910331545dd2fc086b58c37170 Mon Sep 17 00:00:00 2001 From: Kshitij Sisodia Date: Fri, 6 May 2022 09:13:03 +0100 Subject: 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 --- .../api/use_case/noise_reduction/CMakeLists.txt | 40 + .../include/RNNoiseFeatureProcessor.hpp | 341 ++++++++ .../noise_reduction/include/RNNoiseModel.hpp | 78 ++ .../noise_reduction/include/RNNoiseProcessing.hpp | 113 +++ .../noise_reduction/src/RNNoiseFeatureProcessor.cc | 892 +++++++++++++++++++++ .../use_case/noise_reduction/src/RNNoiseModel.cc | 96 +++ .../noise_reduction/src/RNNoiseProcessing.cc | 100 +++ 7 files changed, 1660 insertions(+) create mode 100644 source/application/api/use_case/noise_reduction/CMakeLists.txt create mode 100644 source/application/api/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp create mode 100644 source/application/api/use_case/noise_reduction/include/RNNoiseModel.hpp create mode 100644 source/application/api/use_case/noise_reduction/include/RNNoiseProcessing.hpp create mode 100644 source/application/api/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc create mode 100644 source/application/api/use_case/noise_reduction/src/RNNoiseModel.cc create mode 100644 source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc (limited to 'source/application/api/use_case/noise_reduction') diff --git a/source/application/api/use_case/noise_reduction/CMakeLists.txt b/source/application/api/use_case/noise_reduction/CMakeLists.txt new file mode 100644 index 0000000..5fa9a73 --- /dev/null +++ b/source/application/api/use_case/noise_reduction/CMakeLists.txt @@ -0,0 +1,40 @@ +#---------------------------------------------------------------------------- +# 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. +#---------------------------------------------------------------------------- +######################################################### +# NOISE REDUCTION API library # +######################################################### +cmake_minimum_required(VERSION 3.15.6) + +set(NOISE_REDUCTION_API_TARGET noise_reduction_api) +project(${NOISE_REDUCTION_API_TARGET} + DESCRIPTION "Noise reduction use case API library" + LANGUAGES C CXX) + +# Create static library +add_library(${NOISE_REDUCTION_API_TARGET} STATIC + src/RNNoiseProcessing.cc + src/RNNoiseFeatureProcessor.cc + src/RNNoiseModel.cc) + +target_include_directories(${NOISE_REDUCTION_API_TARGET} PUBLIC include) + +target_link_libraries(${NOISE_REDUCTION_API_TARGET} PUBLIC common_api) + +message(STATUS "*******************************************************") +message(STATUS "Library : " ${NOISE_REDUCTION_API_TARGET}) +message(STATUS "CMAKE_SYSTEM_PROCESSOR : " ${CMAKE_SYSTEM_PROCESSOR}) +message(STATUS "*******************************************************") diff --git a/source/application/api/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp b/source/application/api/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp new file mode 100644 index 0000000..cbf0e4e --- /dev/null +++ b/source/application/api/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp @@ -0,0 +1,341 @@ +/* + * 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. + */ +#ifndef RNNOISE_FEATURE_PROCESSOR_HPP +#define RNNOISE_FEATURE_PROCESSOR_HPP + +#include "PlatformMath.hpp" +#include +#include +#include +#include + +namespace arm { +namespace app { +namespace rnn { + + using vec1D32F = std::vector; + using vec2D32F = std::vector; + using arrHp = std::array; + using math::FftInstance; + using math::FftType; + + class FrameFeatures { + public: + bool m_silence{false}; /* If frame contains silence or not. */ + vec1D32F m_featuresVec{}; /* Calculated feature vector to feed to model. */ + vec1D32F m_fftX{}; /* Vector of floats arranged to represent complex numbers. */ + vec1D32F m_fftP{}; /* Vector of floats arranged to represent complex numbers. */ + vec1D32F m_Ex{}; /* Spectral band energy for audio x. */ + vec1D32F m_Ep{}; /* Spectral band energy for pitch p. */ + vec1D32F m_Exp{}; /* Correlated spectral energy between x and p. */ + }; + + /** + * @brief RNNoise pre and post processing class based on the 2018 paper from + * Jan-Marc Valin. Recommended reading: + * - https://jmvalin.ca/demo/rnnoise/ + * - https://arxiv.org/abs/1709.08243 + **/ + class RNNoiseFeatureProcessor { + /* Public interface */ + public: + RNNoiseFeatureProcessor(); + ~RNNoiseFeatureProcessor() = default; + + /** + * @brief Calculates the features from a given audio buffer ready to be sent to RNNoise model. + * @param[in] audioData Pointer to the floating point vector + * with audio data (within the numerical + * limits of int16_t type). + * @param[in] audioLen Number of elements in the audio window. + * @param[out] features FrameFeatures object reference. + **/ + void PreprocessFrame(const float* audioData, + size_t audioLen, + FrameFeatures& features); + + /** + * @brief Use the RNNoise model output gain values with pre-processing features + * to generate audio with noise suppressed. + * @param[in] modelOutput Output gain values from model. + * @param[in] features Calculated features from pre-processing step. + * @param[out] outFrame Output frame to be populated. + **/ + void PostProcessFrame(vec1D32F& modelOutput, FrameFeatures& features, vec1D32F& outFrame); + + + /* Public constants */ + public: + static constexpr uint32_t FRAME_SIZE_SHIFT{2}; + static constexpr uint32_t FRAME_SIZE{512}; + static constexpr uint32_t WINDOW_SIZE{2 * FRAME_SIZE}; + static constexpr uint32_t FREQ_SIZE{FRAME_SIZE + 1}; + + static constexpr uint32_t PITCH_MIN_PERIOD{64}; + static constexpr uint32_t PITCH_MAX_PERIOD{820}; + static constexpr uint32_t PITCH_FRAME_SIZE{1024}; + static constexpr uint32_t PITCH_BUF_SIZE{PITCH_MAX_PERIOD + PITCH_FRAME_SIZE}; + + static constexpr uint32_t NB_BANDS{22}; + static constexpr uint32_t CEPS_MEM{8}; + static constexpr uint32_t NB_DELTA_CEPS{6}; + + static constexpr uint32_t NB_FEATURES{NB_BANDS + 3*NB_DELTA_CEPS + 2}; + + /* Private functions */ + private: + + /** + * @brief Initialises the half window and DCT tables. + */ + void InitTables(); + + /** + * @brief Applies a bi-quadratic filter over the audio window. + * @param[in] bHp Constant coefficient set b (arrHp type). + * @param[in] aHp Constant coefficient set a (arrHp type). + * @param[in,out] memHpX Coefficients populated by this function. + * @param[in,out] audioWindow Floating point vector with audio data. + **/ + void BiQuad( + const arrHp& bHp, + const arrHp& aHp, + arrHp& memHpX, + vec1D32F& audioWindow); + + /** + * @brief Computes features from the "filtered" audio window. + * @param[in] audioWindow Floating point vector with audio data. + * @param[out] features FrameFeatures object reference. + **/ + void ComputeFrameFeatures(vec1D32F& audioWindow, FrameFeatures& features); + + /** + * @brief Runs analysis on the audio buffer. + * @param[in] audioWindow Floating point vector with audio data. + * @param[out] fft Floating point FFT vector containing real and + * imaginary pairs of elements. NOTE: this vector + * does not contain the mirror image (conjugates) + * part of the spectrum. + * @param[out] energy Computed energy for each band in the Bark scale. + * @param[out] analysisMem Buffer sequentially, but partially, + * populated with new audio data. + **/ + void FrameAnalysis( + const vec1D32F& audioWindow, + vec1D32F& fft, + vec1D32F& energy, + vec1D32F& analysisMem); + + /** + * @brief Applies the window function, in-place, over the given + * floating point buffer. + * @param[in,out] x Buffer the window will be applied to. + **/ + void ApplyWindow(vec1D32F& x); + + /** + * @brief Computes the FFT for a given vector. + * @param[in] x Vector to compute the FFT from. + * @param[out] fft Floating point FFT vector containing real and + * imaginary pairs of elements. NOTE: this vector + * does not contain the mirror image (conjugates) + * part of the spectrum. + **/ + void ForwardTransform( + vec1D32F& x, + vec1D32F& fft); + + /** + * @brief Computes band energy for each of the 22 Bark scale bands. + * @param[in] fft_X FFT spectrum (as computed by ForwardTransform). + * @param[out] bandE Vector with 22 elements populated with energy for + * each band. + **/ + void ComputeBandEnergy(const vec1D32F& fft_X, vec1D32F& bandE); + + /** + * @brief Computes band energy correlation. + * @param[in] X FFT vector X. + * @param[in] P FFT vector P. + * @param[out] bandC Vector with 22 elements populated with band energy + * correlation for the two input FFT vectors. + **/ + void ComputeBandCorr(const vec1D32F& X, const vec1D32F& P, vec1D32F& bandC); + + /** + * @brief Performs pitch auto-correlation for a given vector for + * given lag. + * @param[in] x Input vector. + * @param[out] ac Auto-correlation output vector. + * @param[in] lag Lag value. + * @param[in] n Number of elements to consider for correlation + * computation. + **/ + void AutoCorr(const vec1D32F &x, + vec1D32F &ac, + size_t lag, + size_t n); + + /** + * @brief Computes pitch cross-correlation. + * @param[in] x Input vector 1. + * @param[in] y Input vector 2. + * @param[out] xCorr Cross-correlation output vector. + * @param[in] len Number of elements to consider for correlation. + * computation. + * @param[in] maxPitch Maximum pitch. + **/ + void PitchXCorr( + const vec1D32F& x, + const vec1D32F& y, + vec1D32F& xCorr, + size_t len, + size_t maxPitch); + + /** + * @brief Computes "Linear Predictor Coefficients". + * @param[in] ac Correlation vector. + * @param[in] p Number of elements of input vector to consider. + * @param[out] lpc Output coefficients vector. + **/ + void LPC(const vec1D32F& ac, int32_t p, vec1D32F& lpc); + + /** + * @brief Custom FIR implementation. + * @param[in] num FIR coefficient vector. + * @param[in] N Number of elements. + * @param[out] x Vector to be be processed. + **/ + void Fir5(const vec1D32F& num, uint32_t N, vec1D32F& x); + + /** + * @brief Down-sample the pitch buffer. + * @param[in,out] pitchBuf Pitch buffer. + * @param[in] pitchBufSz Buffer size. + **/ + void PitchDownsample(vec1D32F& pitchBuf, size_t pitchBufSz); + + /** + * @brief Pitch search function. + * @param[in] xLP Shifted pitch buffer input. + * @param[in] y Pitch buffer input. + * @param[in] len Length to search for. + * @param[in] maxPitch Maximum pitch. + * @return pitch index. + **/ + int PitchSearch(vec1D32F& xLp, vec1D32F& y, uint32_t len, uint32_t maxPitch); + + /** + * @brief Finds the "best" pitch from the buffer. + * @param[in] xCorr Pitch correlation vector. + * @param[in] y Pitch buffer input. + * @param[in] len Length to search for. + * @param[in] maxPitch Maximum pitch. + * @return pitch array (2 elements). + **/ + arrHp FindBestPitch(vec1D32F& xCorr, vec1D32F& y, uint32_t len, uint32_t maxPitch); + + /** + * @brief Remove pitch period doubling errors. + * @param[in,out] pitchBuf Pitch buffer vector. + * @param[in] maxPeriod Maximum period. + * @param[in] minPeriod Minimum period. + * @param[in] frameSize Frame size. + * @param[in] pitchIdx0_ Pitch index 0. + * @return pitch index. + **/ + int RemoveDoubling( + vec1D32F& pitchBuf, + uint32_t maxPeriod, + uint32_t minPeriod, + uint32_t frameSize, + size_t pitchIdx0_); + + /** + * @brief Computes pitch gain. + * @param[in] xy Single xy cross correlation value. + * @param[in] xx Single xx auto correlation value. + * @param[in] yy Single yy auto correlation value. + * @return Calculated pitch gain. + **/ + float ComputePitchGain(float xy, float xx, float yy); + + /** + * @brief Computes DCT vector from the given input. + * @param[in] input Input vector. + * @param[out] output Output vector with DCT coefficients. + **/ + void DCT(vec1D32F& input, vec1D32F& output); + + /** + * @brief Perform inverse fourier transform on complex spectral vector. + * @param[out] out Output vector. + * @param[in] fftXIn Vector of floats arranged to represent complex numbers interleaved. + **/ + void InverseTransform(vec1D32F& out, vec1D32F& fftXIn); + + /** + * @brief Perform pitch filtering. + * @param[in] features Object with pre-processing calculated frame features. + * @param[in] g Gain values. + **/ + void PitchFilter(FrameFeatures& features, vec1D32F& g); + + /** + * @brief Interpolate the band gain values. + * @param[out] g Gain values. + * @param[in] bandE Vector with 22 elements populated with energy for + * each band. + **/ + void InterpBandGain(vec1D32F& g, vec1D32F& bandE); + + /** + * @brief Create de-noised frame. + * @param[out] outFrame Output vector for storing the created audio frame. + * @param[in] fftY Gain adjusted complex spectral vector. + */ + void FrameSynthesis(vec1D32F& outFrame, vec1D32F& fftY); + + /* Private objects */ + private: + FftInstance m_fftInstReal; /* FFT instance for real numbers */ + FftInstance m_fftInstCmplx; /* FFT instance for complex numbers */ + vec1D32F m_halfWindow; /* Window coefficients */ + vec1D32F m_dctTable; /* DCT table */ + vec1D32F m_analysisMem; /* Buffer used for frame analysis */ + vec2D32F m_cepstralMem; /* Cepstral coefficients */ + size_t m_memId; /* memory ID */ + vec1D32F m_synthesisMem; /* Synthesis mem (used by post-processing) */ + vec1D32F m_pitchBuf; /* Pitch buffer */ + float m_lastGain; /* Last gain calculated */ + int m_lastPeriod; /* Last period calculated */ + arrHp m_memHpX; /* HpX coefficients. */ + vec1D32F m_lastGVec; /* Last gain vector (used by post-processing) */ + + /* Constants */ + const std::array m_eband5ms { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, + 14, 16, 20, 24, 28, 34, 40, 48, 60, 78, 100}; + }; + + +} /* namespace rnn */ +} /* namespace app */ +} /* namespace arm */ + +#endif /* RNNOISE_FEATURE_PROCESSOR_HPP */ diff --git a/source/application/api/use_case/noise_reduction/include/RNNoiseModel.hpp b/source/application/api/use_case/noise_reduction/include/RNNoiseModel.hpp new file mode 100644 index 0000000..3d2f23c --- /dev/null +++ b/source/application/api/use_case/noise_reduction/include/RNNoiseModel.hpp @@ -0,0 +1,78 @@ +/* + * 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. + */ +#ifndef RNNOISE_MODEL_HPP +#define RNNOISE_MODEL_HPP + +#include "Model.hpp" + +extern const uint32_t g_NumInputFeatures; +extern const uint32_t g_FrameLength; +extern const uint32_t g_FrameStride; + +namespace arm { +namespace app { + + class RNNoiseModel : public Model { + public: + /** + * @brief Runs inference for RNNoise model. + * + * Call CopyGruStates so GRU state outputs are copied to GRU state inputs before the inference run. + * Run ResetGruState() method to set states to zero before starting processing logically related data. + * @return True if inference succeeded, False - otherwise + */ + bool RunInference() override; + + /** + * @brief Sets GRU input states to zeros. + * Call this method before starting processing the new sequence of logically related data. + */ + void ResetGruState(); + + /** + * @brief Copy current GRU output states to input states. + * Call this method before starting processing the next sequence of logically related data. + */ + bool CopyGruStates(); + + /* Which index of model outputs does the main output (gains) come from. */ + const size_t m_indexForModelOutput = 1; + + protected: + /** @brief Gets the reference to op resolver interface class. */ + const tflite::MicroOpResolver& GetOpResolver() override; + + /** @brief Adds operations to the op resolver instance. */ + bool EnlistOperations() override; + + /* + Each inference after the first needs to copy 3 GRU states from a output index to input index (model dependent): + 0 -> 3, 2 -> 2, 3 -> 1 + */ + const std::vector> m_gruStateMap = {{0,3}, {2, 2}, {3, 1}}; + private: + /* Maximum number of individual operations that can be enlisted. */ + static constexpr int ms_maxOpCnt = 15; + + /* A mutable op resolver instance. */ + tflite::MicroMutableOpResolver m_opResolver; + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* RNNOISE_MODEL_HPP */ diff --git a/source/application/api/use_case/noise_reduction/include/RNNoiseProcessing.hpp b/source/application/api/use_case/noise_reduction/include/RNNoiseProcessing.hpp new file mode 100644 index 0000000..15e62d9 --- /dev/null +++ b/source/application/api/use_case/noise_reduction/include/RNNoiseProcessing.hpp @@ -0,0 +1,113 @@ +/* + * 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. + */ +#ifndef RNNOISE_PROCESSING_HPP +#define RNNOISE_PROCESSING_HPP + +#include "BaseProcessing.hpp" +#include "Model.hpp" +#include "RNNoiseFeatureProcessor.hpp" + +namespace arm { +namespace app { + + /** + * @brief Pre-processing class for Noise Reduction use case. + * Implements methods declared by BasePreProcess and anything else needed + * to populate input tensors ready for inference. + */ + class RNNoisePreProcess : public BasePreProcess { + + public: + /** + * @brief Constructor + * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. + * @param[in/out] featureProcessor RNNoise specific feature extractor object. + * @param[in/out] frameFeatures RNNoise specific features shared between pre & post-processing. + * + **/ + explicit RNNoisePreProcess(TfLiteTensor* inputTensor, + std::shared_ptr featureProcessor, + std::shared_ptr frameFeatures); + + /** + * @brief Should perform pre-processing of 'raw' input audio data and load it into + * TFLite Micro input tensors ready for inference + * @param[in] input Pointer to the data that pre-processing will work on. + * @param[in] inputSize Size of the input data. + * @return true if successful, false otherwise. + **/ + bool DoPreProcess(const void* input, size_t inputSize) override; + + private: + TfLiteTensor* m_inputTensor; /* Model input tensor. */ + std::shared_ptr m_featureProcessor; /* RNNoise feature processor shared between pre & post-processing. */ + std::shared_ptr m_frameFeatures; /* RNNoise features shared between pre & post-processing. */ + rnn::vec1D32F m_audioFrame; /* Audio frame cast to FP32 */ + + /** + * @brief Quantize the given features and populate the input Tensor. + * @param[in] inputFeatures Vector of floating point features to quantize. + * @param[in] quantScale Quantization scale for the inputTensor. + * @param[in] quantOffset Quantization offset for the inputTensor. + * @param[in,out] inputTensor TFLite micro tensor to populate. + **/ + static void QuantizeAndPopulateInput(rnn::vec1D32F& inputFeatures, + float quantScale, int quantOffset, + TfLiteTensor* inputTensor); + }; + + /** + * @brief Post-processing class for Noise Reduction use case. + * Implements methods declared by BasePostProcess and anything else needed + * to populate result vector. + */ + class RNNoisePostProcess : public BasePostProcess { + + public: + /** + * @brief Constructor + * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. + * @param[out] denoisedAudioFrame Vector to store the final denoised audio frame. + * @param[in/out] featureProcessor RNNoise specific feature extractor object. + * @param[in/out] frameFeatures RNNoise specific features shared between pre & post-processing. + **/ + RNNoisePostProcess(TfLiteTensor* outputTensor, + std::vector& denoisedAudioFrame, + std::shared_ptr featureProcessor, + std::shared_ptr frameFeatures); + + /** + * @brief Should perform post-processing of the result of inference then + * populate result data for any later use. + * @return true if successful, false otherwise. + **/ + bool DoPostProcess() override; + + private: + TfLiteTensor* m_outputTensor; /* Model output tensor. */ + std::vector& m_denoisedAudioFrame; /* Vector to store the final denoised frame. */ + rnn::vec1D32F m_denoisedAudioFrameFloat; /* Internal vector to store the final denoised frame (FP32). */ + std::shared_ptr m_featureProcessor; /* RNNoise feature processor shared between pre & post-processing. */ + std::shared_ptr m_frameFeatures; /* RNNoise features shared between pre & post-processing. */ + std::vector m_modelOutputFloat; /* Internal vector to store de-quantized model output. */ + + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* RNNOISE_PROCESSING_HPP */ \ No newline at end of file diff --git a/source/application/api/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc b/source/application/api/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc new file mode 100644 index 0000000..036894c --- /dev/null +++ b/source/application/api/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc @@ -0,0 +1,892 @@ +/* + * 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 +#include +#include + +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 outputBands = modelOutput; + std::vector 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(PITCH_BUF_SIZE) - static_cast(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(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(logMax - 7, std::max(follow - 1.5, Ly[i])); + logMax = std::max(logMax, Ly[i]); + follow = std::max(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(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(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(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 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(this->m_lastPeriod)/2; + float lastGain = static_cast(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 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 r(NB_BANDS, 0); + std::vector rf(FREQ_SIZE, 0); + std::vector 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 norm(NB_BANDS); + std::vector 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 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(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 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/application/api/use_case/noise_reduction/src/RNNoiseModel.cc b/source/application/api/use_case/noise_reduction/src/RNNoiseModel.cc new file mode 100644 index 0000000..457cda9 --- /dev/null +++ b/source/application/api/use_case/noise_reduction/src/RNNoiseModel.cc @@ -0,0 +1,96 @@ +/* + * 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 (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; + } + return true; +} + +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(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>> 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 tempOutGruState(outputGruStateTensor->bytes); + auto* outGruState = tflite::GetTensorData(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(inputGruStateTensor); + auto* outGruState = outputGruStateTensorData.data(); + memcpy(inputGruState, outGruState, inputGruStateTensor->bytes); + } + return true; +} diff --git a/source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc b/source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc new file mode 100644 index 0000000..f6a3ec4 --- /dev/null +++ b/source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc @@ -0,0 +1,100 @@ +/* + * 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 featureProcessor, std::shared_ptr 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(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::min(); + const float maxVal = std::numeric_limits::max(); + + auto* inputTensorData = tflite::GetTensorData(inputTensor); + + for (size_t i=0; i < inputFeatures.size(); ++i) { + float quantValue = ((inputFeatures[i] / quantScale) + quantOffset); + inputTensorData[i] = static_cast(std::min(std::max(quantValue, minVal), maxVal)); + } + } + + RNNoisePostProcess::RNNoisePostProcess(TfLiteTensor* outputTensor, + std::vector& denoisedAudioFrame, + std::shared_ptr featureProcessor, + std::shared_ptr 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(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(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( + std::roundf(this->m_denoisedAudioFrameFloat[i])); + } + + return true; + } + +} /* namespace app */ +} /* namespace arm */ \ No newline at end of file -- cgit v1.2.1