Add State Transition Network Hysteresis Mode (#111) - AnalogTapeModel - Physical modelling signal processing for analog tape recording

commit 44ede9ff2f8b27d95b179ebec0ef34e69437945c
parent 87f22f40785fa15ea5cda07d113423f7806f8b85
Author: jatinchowdhury18 <jatinchowdhury18@gmail.com>
Date:   Tue, 24 Nov 2020 12:39:33 -0800

Add State Transition Network Hysteresis Mode (#111)

* Set up hysteresis STN with working drove parameter

* Update benchmarks

* Implement STNs with models for saturation param

* Add more STN models

* Update trained STN models

* Update STN models

* Update docs

* Update Travis

Co-authored-by: jatinchowdhury18 <jatinchowdhury18@users.noreply.github.com>
Diffstat:
M .travis.yml  | 2 +-
M Manual/ChowTapeManual.pdf  | 0 
M Manual/ChowTapeManual.tex  | 8 +++++++-
M Plugin/Bench/Source/Main.cpp  | 10 +++++++++-
M Plugin/CHOWTapeModel.jucer  | 46 ++++++++++++++++++++++++++++++++++++++++++++--
M Plugin/Source/Processors/Hysteresis/HysteresisProcessing.cpp  | 24 ++++++++++++++++++++++--
M Plugin/Source/Processors/Hysteresis/HysteresisProcessing.h  | 9 ++++++++-
M Plugin/Source/Processors/Hysteresis/HysteresisProcessor.cpp  | 4 ++--
A Plugin/Source/Processors/Hysteresis/HysteresisSTN.cpp  | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/HysteresisSTN.h  | 40 ++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/.gitignore  | 4 ++++
A Plugin/Source/Processors/Hysteresis/RTNeural/CMakeLists.txt  | 20 ++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/LICENSE  | 29 +++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/README.md  | 15 +++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/CMakeLists.txt  | 13 +++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.cpp  | 173 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.h  | 25 +++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/Layer.h  | 29 +++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/Model.h  | 80 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/activation.h  | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/dense.h  | 120 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/dense_eigen.h  | 73 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.cpp  | 158 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.h  | 88 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.cpp  | 111 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.h  | 73 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/tests/CMakeLists.txt  | 6 ++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/tests/dense.cpp  | 37 +++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/tests/load_csv.hpp  | 32 ++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/RTNeural/tests/model_loader.hpp  | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_0.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_10.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_100.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_20.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_30.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_40.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_50.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_60.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_70.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_80.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_90.json  | 2628 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

41 files changed, 30388 insertions(+), 10 deletions(-)
diff --git a/.travis.yml b/.travis.yml
@@ -76,7 +76,7 @@ script:
       curl -L "https://github.com/Tracktion/pluginval/releases/download/latest_release/pluginval_macOS.zip" -o pluginval.zip
       unzip pluginval
       pluginval_command="pluginval.app/Contents/MacOS/pluginval --strictness-level 8 --validate-in-process --validate '${TRAVIS_BUILD_DIR}/Plugin/Builds/MacOSX/build/Release/CHOWTapeModel.vst3'"
-      max_retry=5
+      max_retry=2
       counter=0
       until $pluginval_command
       do 
diff --git a/Manual/ChowTapeManual.pdf b/Manual/ChowTapeManual.pdf
Binary files differ.
diff --git a/Manual/ChowTapeManual.tex b/Manual/ChowTapeManual.tex
@@ -158,13 +158,17 @@ currently supports the following hysteresis modes:
     \item 4th-order Runge Kutta (RK4)
     \item 4-iteration Newton Raphson (NR4)
     \item 8-iteration Newton Raphson (NR8)
+    \item State Transition Network\footnote{Parker, J.D. et. al. (2019) ``Modelling of Nonlinear State-Space Systems using a Deep Neural Network'' \textit{Proc. 22\textsuperscript{nd} Int. Conference on Digital Audio Effects}.} (STN)
     \item Version 1.0 processing (V1)
 \end{itemize}
 %
 The Runge-Kutta solvers are computationally cheaper, but
 somewhat less accurate than the Newton-Raphson solvers.
 Similarly, the higher-order solvers will be more accurate,
-but will also consume more compute resources. The V1 mode
+but will also consume more compute resources. The State
+Transition Network is designed to be a computationally
+cheaper approximation of the NR8 solver; although it
+distorts more harshly at extreme settings. The V1 mode
 reverts to a different parameterization of the hysteresis
 equation that was used in earlier versions of the plugin. It
 is recommended to use higher-order solvers for mix busses
@@ -232,6 +236,8 @@ tape. \boldtheme{Depth} and \boldtheme{Amount} control the amount
 of degradation that is added to the tape, while \boldtheme{Variance}
 adds a time-varying randomness to the degradatation.
 
+\vspace{2em}
+
 \subsubsection{Chew Controls}
 The chew parameters simulate tape that has been chewed up by
 a broken tape machine. \boldtheme{Depth} controls how deep the
diff --git a/Plugin/Bench/Source/Main.cpp b/Plugin/Bench/Source/Main.cpp
@@ -41,10 +41,18 @@ int main (int argc, char* argv[])
     for (auto param : params)
     {
         if (param->getName (10) == "Oversampling")
+        {
             param->setValue (3.0f / 4.0f); // 8x
+            std::cout << "Setting parameter " << param->getName (10)
+                << ": " << param->getText (param->getValue(), 1024) << std::endl;
+        }
 
         if (param->getName (10) == "Mode")
-            param->setValue (3.0f / 5.0f); // NR4
+        {
+            param->setValue (4.0f / 5.0f); // STN
+            std::cout << "Setting parameter " << param->getName (10)
+                << ": " << param->getText (param->getValue(), 1024) << std::endl;
+        }
     }
 
     // process audio
diff --git a/Plugin/CHOWTapeModel.jucer b/Plugin/CHOWTapeModel.jucer
@@ -111,6 +111,45 @@
                 file="Source/Processors/Degrade/DegradeProcessor.h"/>
         </GROUP>
         <GROUP id="{6052B1B0-83EF-DBFA-991C-FC0B47A949C9}" name="Hysteresis">
+          <GROUP id="{A4185D0A-8289-D298-5C8F-340D5F34536D}" name="RTNeural">
+            <FILE id="VYlPdE" name="activation.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/activation.h"/>
+            <FILE id="Rvs0tO" name="dense.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/dense.h"/>
+            <FILE id="waQbof" name="dense_eigen.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/dense_eigen.h"/>
+            <FILE id="OB1nyt" name="gru.cpp" compile="1" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/gru.cpp"/>
+            <FILE id="CZKQpU" name="gru.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/gru.h"/>
+            <FILE id="mBKfVC" name="gru_eigen.cpp" compile="1" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/gru_eigen.cpp"/>
+            <FILE id="AGa9yU" name="gru_eigen.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/gru_eigen.h"/>
+            <FILE id="idA6lp" name="Json2RnnParser.cpp" compile="1" resource="0"
+                  file="Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.cpp"/>
+            <FILE id="ZN3kzT" name="Json2RnnParser.h" compile="0" resource="0"
+                  file="Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.h"/>
+            <FILE id="G6NDg6" name="Layer.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/Layer.h"/>
+            <FILE id="b8hfRa" name="Model.h" compile="0" resource="0" file="Source/Processors/Hysteresis/RTNeural/src/Model.h"/>
+          </GROUP>
+          <GROUP id="{875CE76D-E0F5-EAD7-C837-F2D490D96C63}" name="STN_Models">
+            <FILE id="PwcQg5" name="hyst_width_0.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_0.json"/>
+            <FILE id="tOwEot" name="hyst_width_10.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_10.json"/>
+            <FILE id="VCIoxi" name="hyst_width_20.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_20.json"/>
+            <FILE id="HSGyJo" name="hyst_width_30.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_30.json"/>
+            <FILE id="TQ4HqA" name="hyst_width_40.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_40.json"/>
+            <FILE id="d8DzmO" name="hyst_width_50.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_50.json"/>
+            <FILE id="iLqOdz" name="hyst_width_60.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_60.json"/>
+            <FILE id="P39ERI" name="hyst_width_70.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_70.json"/>
+            <FILE id="CCn8WH" name="hyst_width_80.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_80.json"/>
+            <FILE id="ihUN3y" name="hyst_width_90.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_90.json"/>
+            <FILE id="xtDfPX" name="hyst_width_100.json" compile="0" resource="1"
+                  file="Source/Processors/Hysteresis/STN_Models/hyst_width_100.json"/>
+          </GROUP>
           <FILE id="l6IKp3" name="DCBlocker.h" compile="0" resource="0" file="Source/Processors/Hysteresis/DCBlocker.h"/>
           <FILE id="Qe4tlV" name="HysteresisProcessing.cpp" compile="1" resource="0"
                 file="Source/Processors/Hysteresis/HysteresisProcessing.cpp"/>
@@ -120,6 +159,9 @@
                 file="Source/Processors/Hysteresis/HysteresisProcessor.cpp"/>
           <FILE id="TRDp2E" name="HysteresisProcessor.h" compile="0" resource="0"
                 file="Source/Processors/Hysteresis/HysteresisProcessor.h"/>
+          <FILE id="yyRNO0" name="HysteresisSTN.cpp" compile="1" resource="0"
+                file="Source/Processors/Hysteresis/HysteresisSTN.cpp"/>
+          <FILE id="ELL2Q2" name="HysteresisSTN.h" compile="0" resource="0" file="Source/Processors/Hysteresis/HysteresisSTN.h"/>
           <FILE id="yqa9LH" name="ToneControl.cpp" compile="1" resource="0" file="Source/Processors/Hysteresis/ToneControl.cpp"/>
           <FILE id="vd8RTB" name="ToneControl.h" compile="0" resource="0" file="Source/Processors/Hysteresis/ToneControl.h"/>
           <FILE id="Gxo9aB" name="ToneFilter.h" compile="0" resource="0" file="Source/Processors/Hysteresis/ToneFilter.h"/>
@@ -178,10 +220,10 @@
         <MODULEPATH id="juce_opengl" path="Juce/modules"/>
       </MODULEPATHS>
     </XCODE_MAC>
-    <VS2017 targetFolder="Builds/VisualStudio2017">
+    <VS2017 targetFolder="Builds/VisualStudio2017" extraCompilerFlags="/wd4458">
       <CONFIGURATIONS>
         <CONFIGURATION isDebug="1" name="Debug"/>
-        <CONFIGURATION isDebug="0" name="Release"/>
+        <CONFIGURATION name="Release"/>
         <CONFIGURATION isDebug="0" name="Release32" winArchitecture="Win32"/>
       </CONFIGURATIONS>
       <MODULEPATHS>
diff --git a/Plugin/Source/Processors/Hysteresis/HysteresisProcessing.cpp b/Plugin/Source/Processors/Hysteresis/HysteresisProcessing.cpp
@@ -1,5 +1,6 @@
-#include "HysteresisProcessing.h"
 #include <math.h>
+#include "HysteresisProcessing.h"
+#include "RTNeural/src/Json2RnnParser.h"
 
 namespace
 {
@@ -32,10 +33,13 @@ void HysteresisProcessing::setSampleRate (double newSR)
     fs = newSR;
     T = 1.0 / fs;
     Talpha = T / 1.9;
+    hysteresisSTN.prepare (newSR);
 }
 
 void HysteresisProcessing::cook (float drive, float width, float sat, bool v1)
 {
+    hysteresisSTN.setParams (sat, width);
+
     M_s = 0.5 + 1.5 * (1.0 - (double) sat);
     a = M_s / (0.01 + 6.0 * (double) drive);
     c = std::sqrt (1.0f - (double) width) - 0.01;
@@ -72,6 +76,10 @@ void HysteresisProcessing::setSolver (SolverType solverType)
         solver = &HysteresisProcessing::RK4;
         return;
 
+    case SolverType::STN:
+        solver = &HysteresisProcessing::STN;
+        return;
+
     case SolverType::NR4:
         numIter = 4;
         return;
@@ -131,7 +139,7 @@ inline double HysteresisProcessing::hysteresisFunc (double M, double H, double H
     return H_d * (f1 + f2) / f3;
 }
 
-inline double HysteresisProcessing::hysteresisFuncPrime (double H_d, double dMdt) noexcept
+inline double HysteresisProcessing::hysteresisFuncPrime (double H_d, double dMdt) const noexcept
 {
     const double L_prime2 = langevinD2 (Q);
     const double M_diff2 = M_s_oa_talpha * L_prime - 1.0;
@@ -199,3 +207,15 @@ inline double HysteresisProcessing::NR (double H, double H_d) noexcept
 
     return M;
 }
+
+inline double HysteresisProcessing::STN (double H, double H_d) noexcept
+{
+    std::array<double, HysteresisSTN::inputSize> input { H, H_d, H_n1, H_d_n1, M_n1 };
+
+    // scale derivatives
+    input[1] *= HysteresisSTN::diffMakeup;
+    input[3] *= HysteresisSTN::diffMakeup;
+    FloatVectorOperations::multiply (input.data(), 0.7071 / a, 4); // scale by drive param
+
+    return hysteresisSTN.process (input) + M_n1;
+}
diff --git a/Plugin/Source/Processors/Hysteresis/HysteresisProcessing.h b/Plugin/Source/Processors/Hysteresis/HysteresisProcessing.h
@@ -2,6 +2,7 @@
 #define HYSTERESISPROCESSING_H_INCLUDED
 
 #include "JuceHeader.h"
+#include "HysteresisSTN.h"
 
 enum SolverType
 {
@@ -9,6 +10,8 @@ enum SolverType
     RK4,
     NR4,
     NR8,
+    STN,
+    NUM_SOLVERS
 };
 
 /*
@@ -59,7 +62,7 @@ private:
     inline double hysteresisFunc (double M, double H, double H_d) noexcept;
 
     // derivative of hysteresis func w.r.t M (depends on cached values from computing hysteresisFunc)
-    inline double hysteresisFuncPrime (double H_d, double dMdt) noexcept;
+    inline double hysteresisFuncPrime (double H_d, double dMdt) const noexcept;
 
     // runge-kutta solvers
     inline double RK2 (double H, double H_d) noexcept;
@@ -69,6 +72,10 @@ private:
     inline double NR (double H, double H_d) noexcept;
     int numIter = 0;
 
+    // state transition network solver
+    inline double STN (double H, double H_d) noexcept;
+    HysteresisSTN hysteresisSTN;
+
     // solver function pointer
     using Solver = double (HysteresisProcessing::*) (double, double);
     Solver solver;
diff --git a/Plugin/Source/Processors/Hysteresis/HysteresisProcessor.cpp b/Plugin/Source/Processors/Hysteresis/HysteresisProcessor.cpp
@@ -33,14 +33,14 @@ void HysteresisProcessor::createParameterLayout (std::vector<std::unique_ptr<Ran
     params.push_back (std::make_unique<AudioParameterFloat> ("sat", "Saturation", 0.0f, 1.0f, 0.5f));
     params.push_back (std::make_unique<AudioParameterFloat> ("width", "Bias", 0.0f, 1.0f, 0.5f));
 
-    params.push_back (std::make_unique<AudioParameterChoice> ("mode", "Mode", StringArray ({"RK2", "RK4", "NR4", "NR8", "V1"}), 0));
+    params.push_back (std::make_unique<AudioParameterChoice> ("mode", "Mode", StringArray ({"RK2", "RK4", "NR4", "NR8", "STN", "V1"}), 0));
     params.push_back (std::make_unique<AudioParameterChoice> ("os", "Oversampling", StringArray ({"1x", "2x", "4x", "8x", "16x"}), 1));
     params.push_back (std::make_unique<AudioParameterBool>   ("hyst_onoff", "On/Off", true));
 }
 
 void HysteresisProcessor::setSolver (int newSolver)
 {
-    if (newSolver == 4) // V1
+    if (newSolver == SolverType::NUM_SOLVERS) // V1
     {
         useV1 = true;
         newSolver = 1; // RK4
diff --git a/Plugin/Source/Processors/Hysteresis/HysteresisSTN.cpp b/Plugin/Source/Processors/Hysteresis/HysteresisSTN.cpp
@@ -0,0 +1,86 @@
+#include <future>
+#include "HysteresisSTN.h"
+#include "RTNeural/src/Json2RnnParser.h"
+
+namespace
+{
+    constexpr double trainingSampleRate = 96e3;
+    
+    constexpr float satIdxMult   = (float) HysteresisSTN::numSatModels   - 1.0f;
+    constexpr float widthIdxMult = (float) HysteresisSTN::numWidthModels - 1.0f;
+
+    static std::array<String, HysteresisSTN::numWidthModels> widthTags
+        { "0", "10", "20", "30", "40", "50", "60", "70", "80", "90", "100"};
+
+    static std::array<String, HysteresisSTN::numSatModels> satTags
+        { "0",  "5", "10", "15", "20", "25", "30", "35", "40", "45",
+         "50", "55", "60", "65", "70", "75", "80", "85", "90", "95", "100" };
+}
+
+constexpr size_t getSatIdx (float satParam)
+{
+    return std::clamp (static_cast<int> (satIdxMult * satParam), 0, HysteresisSTN::numSatModels - 1);
+}
+
+constexpr size_t getWidthIdx (float widthParam)
+{
+    return std::clamp (static_cast<int> (widthIdxMult * widthParam), 0, HysteresisSTN::numWidthModels - 1);
+}
+
+std::unique_ptr<MemoryInputStream> getModelFileStream (const String& modelFile)
+{
+    std::unique_ptr<MemoryInputStream> stream;
+    for (int i = 0; i < BinaryData::namedResourceListSize; ++i)
+    {
+        if (String (BinaryData::originalFilenames[i]) == modelFile)
+        {
+            int fileSize = 0;
+            auto* fileData = BinaryData::getNamedResource (BinaryData::namedResourceList[i], fileSize);
+            stream = std::make_unique<MemoryInputStream> (fileData, fileSize, false);
+            return std::move (stream);
+        }
+    }
+
+    return {};
+}
+
+HysteresisSTN::HysteresisSTN()
+{
+    // load models
+    auto start = Time::getMillisecondCounterHiRes();
+    size_t widthLoadIdx = 0;
+    for (const auto& width : widthTags)
+    {
+        auto modelsStream = getModelFileStream ("hyst_width_" + width + ".json");
+        jassert (modelsStream != nullptr);
+
+        auto modelsJson = JSON::parse (*modelsStream.get());
+        size_t satLoadIdx = 0;
+        for (const auto& sat : satTags)
+        {
+            String modelTag = "drive_" + sat + "_" + width;
+            auto modelJson = modelsJson[modelTag.toRawUTF8()];
+            stnModels[widthLoadIdx][satLoadIdx] = Json2RnnParser::parseJson<double> (modelJson);
+
+            jassert (stnModels[widthLoadIdx][satLoadIdx] != nullptr);
+            jassert (stnModels[widthLoadIdx][satLoadIdx]->layers[0]->in_size == inputSize);
+            satLoadIdx++;
+        }
+        widthLoadIdx++;
+    }
+
+    auto dur = Time::getMillisecondCounterHiRes() - start;
+    Logger::writeToLog ("Loaded " + String (widthTags.size() * satTags.size())
+        + " models in " + String (dur / 1000.0) + " seconds");
+}
+
+void HysteresisSTN::prepare (double sampleRate)
+{
+    sampleRateCorr = trainingSampleRate / sampleRate;
+}
+
+void HysteresisSTN::setParams (float saturation, float width)
+{
+    satIdx = getSatIdx (saturation);
+    widthIdx = getWidthIdx (width);
+}
diff --git a/Plugin/Source/Processors/Hysteresis/HysteresisSTN.h b/Plugin/Source/Processors/Hysteresis/HysteresisSTN.h
@@ -0,0 +1,40 @@
+#ifndef HYSTERESISSTN_H_INCLUDED
+#define HYSTERESISSTN_H_INCLUDED
+
+#include <JuceHeader.h>
+#include "RTNeural/src/Model.h"
+
+/**
+ * Class that implements a "State Transition Network" for
+ * solving a state space formulation of the hysteresis algorithm.
+ *
+ * For more information, see: Parker et. al., 2019 (http://dafx2019.bcu.ac.uk/papers/DAFx2019_paper_42.pdf)
+ */
+class HysteresisSTN
+{
+public:
+    HysteresisSTN();
+
+    static constexpr size_t inputSize = 5;
+    static constexpr double diffMakeup = 1.0 / 6.0e4;
+
+    void prepare (double sampleRate);
+    void setParams (float saturation, float width);
+
+    inline double process (const std::array<double, inputSize>& input) const noexcept
+    {
+        return stnModels[widthIdx][satIdx]->forward(input.data()) * sampleRateCorr;
+    }
+
+    enum { numWidthModels = 11, numSatModels = 21 };
+
+private:
+    std::unique_ptr<MLUtils::Model<double>> stnModels[numWidthModels][numSatModels];
+    double sampleRateCorr = 1.0;
+    size_t widthIdx = 0;
+    size_t satIdx = 0;
+
+    JUCE_DECLARE_NON_COPYABLE_WITH_LEAK_DETECTOR (HysteresisSTN)
+};
+
+#endif // HYSTERESISSTN_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/.gitignore b/Plugin/Source/Processors/Hysteresis/RTNeural/.gitignore
@@ -0,0 +1,4 @@
+/.vscode
+/build
+__pycache__
+
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/CMakeLists.txt b/Plugin/Source/Processors/Hysteresis/RTNeural/CMakeLists.txt
@@ -0,0 +1,20 @@
+cmake_minimum_required(VERSION 3.8)
+project(MLUtils)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_FLAGS "-O3")
+
+add_definitions(-DUSE_EIGEN)
+
+add_subdirectory(src)
+
+option(BUILD_TESTS "Build MLUtils accuracy tests" OFF)
+if(BUILD_TESTS)
+    message("Building tests...")
+    add_subdirectory(tests)
+endif()
+
+option(BUILD_BENCH "Build MLUtils benchmarks" OFF)
+if(BUILD_BENCH)
+    message("Building benchmarks...")
+endif()
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/LICENSE b/Plugin/Source/Processors/Hysteresis/RTNeural/LICENSE
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2020, jatinchowdhury18
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/README.md b/Plugin/Source/Processors/Hysteresis/RTNeural/README.md
@@ -0,0 +1,15 @@
+# MLUtils
+
+A lightweight neural network inferencing engine written in C++.
+This library was designed with the intention of being used in
+real-time audio processing.
+
+Currently supported layers:
+  
+  - [x] dense
+  - [ ] GRU
+
+Currently supported activations:
+  - [x] tanh
+  - [ ] sigmoid
+  - [ ] ReLU
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/CMakeLists.txt b/Plugin/Source/Processors/Hysteresis/RTNeural/src/CMakeLists.txt
@@ -0,0 +1,13 @@
+include_directories(../modules/Eigen)
+
+add_library(MLUtils STATIC
+    gru.h
+    gru.cpp
+    gru_eigen.h
+    gru_eigen.cpp
+    dense.h
+    dense_eigen.h
+    activation.h
+    Model.h
+    Layer.h
+)
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.cpp b/Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.cpp
@@ -0,0 +1,173 @@
+#include "Json2RnnParser.h"
+
+using namespace MLUtils;
+
+template <typename T>
+std::unique_ptr<Model<T>> Json2RnnParser::parseJson (InputStream& input)
+{
+    auto parent = JSON::parse (input);
+    return parseJson<T> (parent);
+}
+
+template <typename T>
+std::unique_ptr<MLUtils::Model<T>> Json2RnnParser::parseJson (const var& parent)
+{
+    auto shape = parent["in_shape"];
+    auto layers = parent["layers"];
+
+    if (! shape.isArray() || ! layers.isArray())
+        return nullptr;
+
+    auto nDims = int (shape.getArray()->getLast());
+    // std::cout << "# dimensions: " << nDims <<std::endl;
+    auto model = std::make_unique<Model<T>> (nDims);
+
+    for (int i = 0; i < layers.getArray()->size(); ++i)
+    {
+        auto l = layers.getArray()->getUnchecked (i);
+        const auto type = l["type"].toString();
+
+        const auto layerShape = l["shape"];
+        auto layerDims = int (layerShape.getArray()->getLast());
+
+        auto weights = l["weights"];
+        // std::cout << type << " " << layerDims << std::endl;
+
+        if (type == "time-distributed-dense" || type == "dense")
+        {
+            auto dense = createDense<T> (model->getNextInSize(), layerDims, weights);
+            model->addLayer (dense.release());
+
+            const auto activationType = l["activation"].toString();
+            if (activationType == "tanh")
+            {
+                auto activation = std::make_unique<TanhActivation<T>> (layerDims);
+                model->addLayer (activation.release());
+            }
+        }
+        else if (type == "gru")
+        {
+            auto gru = createGRU<T> (model->getNextInSize(), layerDims, weights);
+            model->addLayer (gru.release());
+        }
+        else
+        {
+            jassertfalse;
+        }
+    }
+
+    return std::move (model);
+}
+
+template <typename T>
+std::unique_ptr<Dense<T>> Json2RnnParser::createDense (size_t in_size, size_t out_size, var& weights)
+{
+    auto dense = std::make_unique<Dense<T>> (in_size, out_size);
+    
+    // load kernel weights
+    T** denseWeights;
+    denseWeights = new T* [out_size];
+    for (size_t i = 0; i < out_size; ++i)
+        denseWeights[i] = new T[in_size];
+        
+    auto layerWeights = weights.getArray()->getUnchecked (0);
+    for (int i = 0; i < layerWeights.getArray()->size(); ++i)
+    {
+        auto lw = layerWeights.getArray()->getUnchecked (i);
+        for (int j = 0; j < lw.getArray()->size(); ++j)
+            denseWeights[j][i] = (T) (double) lw.getArray()->getUnchecked (j);
+    }
+
+    dense->setWeights (denseWeights);
+
+    for (size_t i = 0; i < out_size; ++i)
+        delete[] denseWeights[i];
+    delete[] denseWeights;
+
+    // load bias
+    std::vector<T> denseBias (out_size, 0.0f);
+    auto layerBiases = weights.getArray()->getUnchecked (1);
+    for (int i = 0; i < layerBiases.getArray()->size(); ++i)
+        denseBias[i] = (T) (double) layerBiases.getArray()->getUnchecked (i);
+
+    dense->setBias (denseBias.data());
+
+    return std::move (dense);
+}
+
+template <typename T>
+std::unique_ptr<GRULayer<T>> Json2RnnParser::createGRU (size_t in_size, size_t out_size, var& weights)
+{
+    auto gru = std::make_unique<GRULayer<T>> (in_size, out_size);
+
+    // load kernel weights
+    T** kernelWeights;
+    kernelWeights = new T* [in_size];
+    for (size_t i = 0; i < in_size; ++i)
+        kernelWeights[i] = new T[3*out_size];
+        
+    auto layerWeights = weights.getArray()->getUnchecked (0);
+    for (int i = 0; i < layerWeights.getArray()->size(); ++i)
+    {
+        auto lw = layerWeights.getArray()->getUnchecked (i);
+        for (int j = 0; j < lw.getArray()->size(); ++j)
+            kernelWeights[i][j] = (T) (double) lw.getArray()->getUnchecked (j);
+    }
+
+    gru->setWVals (kernelWeights);
+
+    for (size_t i = 0; i < in_size; ++i)
+        delete[] kernelWeights[i];
+    delete[] kernelWeights;
+
+    // load recurrent weights
+    T** recurrentWeights;
+    recurrentWeights = new T* [out_size];
+    for (size_t i = 0; i < out_size; ++i)
+        recurrentWeights[i] = new T[3*out_size];
+        
+    auto layerWeights2 = weights.getArray()->getUnchecked (1);
+    for (int i = 0; i < layerWeights2.getArray()->size(); ++i)
+    {
+        auto lw = layerWeights2.getArray()->getUnchecked (i);
+        for (int j = 0; j < lw.getArray()->size(); ++j)
+            recurrentWeights[i][j] = (T) (double) lw.getArray()->getUnchecked (j);
+    }
+
+    gru->setUVals (recurrentWeights);
+
+    for (size_t i = 0; i < out_size; ++i)
+        delete[] recurrentWeights[i];
+    delete[] recurrentWeights;
+
+    // load biases
+    T** gruBias;
+    gruBias = new T* [2];
+    for (size_t i = 0; i < 2; ++i)
+        gruBias[i] = new T[3*out_size];
+        
+    auto layerBias = weights.getArray()->getUnchecked (2);
+    for (int i = 0; i < layerBias.getArray()->size(); ++i)
+    {
+        auto lw = layerBias.getArray()->getUnchecked (i);
+        for (int j = 0; j < lw.getArray()->size(); ++j)
+            gruBias[i][j] = (T) (double) lw.getArray()->getUnchecked (j);
+    }
+
+    gru->setBVals (gruBias);
+
+    for (size_t i = 0; i < 2; ++i)
+        delete[] gruBias[i];
+    delete[] gruBias;
+
+    return std::move (gru);
+}
+
+template std::unique_ptr<MLUtils::Model<float>> Json2RnnParser::parseJson (InputStream& input);
+template std::unique_ptr<MLUtils::Model<double>> Json2RnnParser::parseJson (InputStream& input);
+
+template std::unique_ptr<Dense<float>> Json2RnnParser::createDense (size_t in_size, size_t out_size, var& weights);
+template std::unique_ptr<Dense<double>> Json2RnnParser::createDense (size_t in_size, size_t out_size, var& weights);
+
+template std::unique_ptr<GRULayer<float>> Json2RnnParser::createGRU (size_t in_size, size_t out_size, var& weights);
+template std::unique_ptr<GRULayer<double>> Json2RnnParser::createGRU (size_t in_size, size_t out_size, var& weights);
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.h
@@ -0,0 +1,25 @@
+#ifndef JSON2RNNPARSER_H_INCLUDED
+#define JSON2RNNPARSER_H_INCLUDED
+
+#include <JuceHeader.h>
+#include "Model.h"
+
+class Json2RnnParser
+{
+public:
+    Json2RnnParser() {}
+
+    template <typename T>
+    static std::unique_ptr<MLUtils::Model<T>> parseJson (InputStream& input);
+
+    template <typename T>
+    static std::unique_ptr<MLUtils::Model<T>> parseJson (const var& json);
+
+    template <typename T>
+    static std::unique_ptr<MLUtils::Dense<T>> createDense (size_t in_size, size_t out_size, var& weights);
+
+    template <typename T>
+    static std::unique_ptr<MLUtils::GRULayer<T>> createGRU (size_t in_size, size_t out_size, var& weights);
+};
+
+#endif // JSON2RNNPARSER_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/Layer.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/Layer.h
@@ -0,0 +1,29 @@
+#ifndef LAYER_H_INCLUDED
+#define LAYER_H_INCLUDED
+
+#include <stddef.h>
+
+namespace MLUtils
+{
+
+/** Neural network layer */
+template<typename T>
+class Layer
+{
+public:
+    Layer (size_t in_size, size_t out_size) :
+        in_size (in_size),
+        out_size (out_size)
+    {}
+
+    virtual ~Layer() {}
+
+    virtual void forward (const T* input, T* out) = 0;
+
+    const size_t in_size;
+    const size_t out_size;
+};
+
+} // namespace MLUtils
+
+#endif // LAYER_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/Model.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/Model.h
@@ -0,0 +1,80 @@
+#ifndef MODEL_H_INCLUDED
+#define MODEL_H_INCLUDED
+
+#include <vector>
+#include <iostream>
+
+#include "Layer.h"
+#include "activation.h"
+#include "dense.h"
+#include "gru.h"
+#include "gru.cpp"
+
+namespace MLUtils
+{
+
+/** Neural network model */
+template<typename T>
+class Model
+{
+public:
+    Model (size_t in_size) :
+        in_size (in_size)
+    {}
+
+    ~Model()
+    {
+        for (auto l : layers)
+            delete l;
+        layers.clear();
+
+        for (auto o : outs)
+            delete[] o;
+        outs.clear();
+    }
+
+    size_t getNextInSize()
+    {
+        if (layers.empty())
+            return in_size;
+        
+        return layers.back()->out_size;
+    }
+
+    void addLayer (Layer<T>* layer)
+    {
+        layers.push_back (layer);
+        outs.push_back (new T[layer->out_size]);
+    }
+
+    void reset()
+    {
+        for (auto* l : layers)
+        {
+            if (auto* lCast = dynamic_cast<GRULayer<T>*> (l))
+                lCast->reset();
+        }
+    }
+
+    inline T forward (const T* input)
+    {
+        layers[0]->forward (input, outs[0]);
+
+        for (size_t i = 1; i < layers.size(); ++i)
+        {
+            layers[i]->forward (outs[i-1], outs[i]);
+        }
+
+        return outs.back()[0];
+    }
+
+    std::vector<Layer<T>*> layers;
+
+private:
+    const size_t in_size;
+    std::vector<T*> outs;
+};
+
+} // namespace MLUtils
+
+#endif // MODEL_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/activation.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/activation.h
@@ -0,0 +1,89 @@
+#ifndef ACTIVATION_H_INCLUDED
+#define ACTIVATION_H_INCLUDED
+
+#include <functional>
+#include "Layer.h"
+
+namespace MLUtils
+{
+
+template<typename T>
+class Activation : public Layer<T>
+{
+public:
+    Activation (size_t size, std::function<T(T)> func) :
+        Layer<T> (size, size),
+        func (func)
+    {}
+
+    virtual ~Activation() {}
+
+    inline void forward (const T* input, T* out) override
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            out[i] = func (input[i]);
+    }
+
+private:
+    const std::function<T(T)> func;
+};
+
+} // namespace MLUtils
+
+#ifdef USE_EIGEN
+#include <Eigen/Dense>
+
+namespace MLUtils
+{
+
+template<typename T>
+class TanhActivation : public Activation<T>
+{
+public:
+    TanhActivation (size_t size) :
+        Activation<T> (size, {})
+    {
+        inVec.resize (size, 1);
+        outVec.resize (size, 1);
+    }
+
+    inline void forward (const T* input, T* out) override
+    {
+        inVec = Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>> (input, Layer<T>::in_size, 1);
+        outVec = inVec.array().tanh();
+
+        std::copy (outVec.data(), outVec.data() + Layer<T>::in_size, out);
+    }
+
+    Eigen::Matrix<T, Eigen::Dynamic, 1> inVec;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> outVec;
+};
+
+} // namespace MLUtils
+
+#else
+#include <cmath>
+
+namespace MLUtils
+{
+
+template<typename T>
+class TanhActivation : public Activation<T>
+{
+public:
+    TanhActivation (size_t size) :
+        Activation<T> (size, [] (T x) { return std::tanh (x); })
+    {}
+
+    inline void forward (const T* input, T* out) override
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            out[i] = std::tanh (input[i]);
+    }
+};
+
+} // namespace MLUtils
+
+#endif // USE_EIGEN
+
+#endif // ACTIVATION_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/dense.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/dense.h
@@ -0,0 +1,120 @@
+#ifndef DENSE_H_INCLUDED
+#define DENSE_H_INCLUDED
+
+#include <vector>
+#include <algorithm>
+#include <numeric>
+
+#ifdef USE_EIGEN
+#include "dense_eigen.h"
+#else
+#include "Layer.h"
+
+namespace MLUtils
+{
+
+template<typename T>
+class Dense1
+{
+public:
+    Dense1 (size_t in_size) :
+        in_size (in_size)
+    {
+        weights = new T[in_size];
+    }
+
+    ~Dense1()
+    {
+        delete[] weights;
+    }
+
+    inline T forward(const T* input)
+    {
+        return std::inner_product(weights, weights + in_size, input, (T) 0) + bias;
+    }
+
+    void setWeights(const T* newWeights)
+    {
+        for (size_t i = 0; i < in_size; ++i)
+            weights[i] = newWeights[i];
+    }
+
+    void setBias(T b) { bias = b; }
+
+    T getWeight(size_t i) const noexcept {
+        return weights[i];
+    }
+
+    T getBias() const noexcept {
+        return bias;
+    }
+
+private:
+    const size_t in_size;
+    T bias;
+
+    T* weights;
+};
+
+template<typename T>
+class Dense : public Layer<T>
+{
+public:
+    Dense (size_t in_size, size_t out_size) :
+        Layer<T> (in_size, out_size)
+    {
+        subLayers = new Dense1<T>*[out_size];
+        for (size_t i = 0; i < out_size; ++i)
+            subLayers[i] = new Dense1<T> (in_size);
+    }
+
+    virtual ~Dense()
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            delete subLayers[i];
+
+        delete[] subLayers;
+    }
+
+    inline void forward (const T* input, T* out) override
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            out[i] = subLayers[i]->forward (input);
+    }
+
+    void setWeights(const std::vector<std::vector<T>>& newWeights)
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            subLayers[i]->setWeights (newWeights[i].data());
+    }
+
+    void setWeights(T** newWeights)
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            subLayers[i]->setWeights (newWeights[i]);
+    }
+
+    void setBias(T* b)
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            subLayers[i]->setBias (b[i]);
+    }
+
+    T getWeight(size_t i, size_t k) const noexcept {
+        return subLayers[i]->getWeight(k);
+    }
+
+
+    T getBias(size_t i) const noexcept {
+        return subLayers[i]->getBias();
+    }
+
+private:
+    Dense1<T>** subLayers;
+};
+
+} // namespace MLUtils
+
+#endif // USE_EIGEN
+
+#endif // DENSE_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/dense_eigen.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/dense_eigen.h
@@ -0,0 +1,73 @@
+#ifndef DENSEEIGEN_H_INCLUDED
+#define DENSEEIGEN_H_INCLUDED
+
+#include <Eigen/Dense>
+#include "Layer.h"
+
+namespace MLUtils
+{
+
+template<typename T>
+class Dense : public Layer<T>
+{
+public:
+    Dense (size_t in_size, size_t out_size) :
+        Layer<T> (in_size, out_size)
+    {
+        weights.resize (out_size, in_size);
+        bias.resize (out_size, 1);
+
+        inVec.resize (in_size, 1);
+        outVec.resize (out_size, 1);
+    }
+
+    virtual ~Dense() {}
+
+    inline void forward (const T* input, T* out) override
+    {
+        inVec = Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>> (input, Layer<T>::in_size, 1);
+        outVec = weights * inVec + bias;
+
+        std::copy (outVec.data(), outVec.data() + Layer<T>::out_size, out);
+    }
+
+    void setWeights(const std::vector<std::vector<T>>& newWeights)
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            for (size_t k = 0; k < Layer<T>::in_size; ++k)
+                weights (i, k) = newWeights[i][k];
+    }
+
+    void setWeights(T** newWeights)
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            for (size_t k = 0; k < Layer<T>::in_size; ++k)
+                weights (i, k) = newWeights[i][k];
+    }
+
+    void setBias(T* b)
+    {
+        for (size_t i = 0; i < Layer<T>::out_size; ++i)
+            bias (i, 0) = b[i];
+    }
+
+    T getWeight(size_t i, size_t k) const noexcept {
+        return weights (i, k);
+    }
+
+
+    T getBias(size_t i) const noexcept {
+        return bias (i, 0);
+    }
+
+private:
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> weights;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> bias;
+
+    Eigen::Matrix<T, Eigen::Dynamic, 1> inVec;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> outVec;
+};
+
+} // namespace MLUtils
+
+#endif // DENSEEIGEN_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.cpp b/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.cpp
@@ -0,0 +1,158 @@
+#include "gru.h"
+
+namespace MLUtils
+{
+
+#ifndef USE_EIGEN
+template<typename T>
+GRULayer<T>::GRULayer (size_t in_size, size_t out_size) :
+    Layer<T> (in_size, out_size),
+    zWeights (in_size, out_size),
+    rWeights (in_size, out_size),
+    cWeights (in_size, out_size)
+{
+    ht1 = new T[out_size];
+    zVec = new T[out_size];
+    rVec = new T[out_size];
+    cVec = new T[out_size];
+}
+
+template<typename T>
+GRULayer<T>::~GRULayer()
+{
+    delete[] ht1;
+    delete[] zVec;
+    delete[] rVec;
+    delete[] cVec;
+}
+
+template<typename T>
+GRULayer<T>::WeightSet::WeightSet (size_t in_size, size_t out_size) :
+    out_size (out_size)
+{
+    W = new T*[out_size];
+    U = new T*[out_size];
+    b[0] = new T[out_size];
+    b[1] = new T[out_size];
+
+    for (size_t i = 0; i < out_size; ++i)
+    {
+        W[i] = new T[in_size];
+        U[i] = new T[out_size];
+    }
+}
+
+template<typename T>
+GRULayer<T>::WeightSet::~WeightSet()
+{
+    delete[] b[0];
+    delete[] b[1];
+
+    for (size_t i = 0; i < out_size; ++i)
+    {
+        delete[] W[i];
+        delete[] U[i];
+    }
+
+    delete[] W;
+    delete[] U;
+}
+
+template<typename T>
+void GRULayer<T>::setWVals(T** wVals)
+{
+    for (size_t i = 0; i < Layer<T>::in_size; ++i)
+    {
+        for (size_t k = 0; k < Layer<T>::out_size; ++k)
+        {
+            zWeights.W[k][i] = wVals[i][k];
+            rWeights.W[k][i] = wVals[i][k+Layer<T>::out_size];
+            cWeights.W[k][i] = wVals[i][k+Layer<T>::out_size*2];
+        }
+    }
+}
+
+template<typename T>
+void GRULayer<T>::setUVals(T** uVals)
+{
+    for (size_t i = 0; i < Layer<T>::out_size; ++i)
+    {
+        for (size_t k = 0; k < Layer<T>::out_size; ++k)
+        {
+            zWeights.U[k][i] = uVals[i][k];
+            rWeights.U[k][i] = uVals[i][k+Layer<T>::out_size];
+            cWeights.U[k][i] = uVals[i][k+Layer<T>::out_size*2];
+        }
+    }
+}
+
+template<typename T>
+void GRULayer<T>::setBVals(T** bVals)
+{
+    for (size_t i = 0; i < 2; ++i)
+    {
+        for (size_t k = 0; k < Layer<T>::out_size; ++k)
+        {
+            zWeights.b[i][k] = bVals[i][k];
+            rWeights.b[i][k] = bVals[i][k+Layer<T>::out_size];
+            cWeights.b[i][k] = bVals[i][k+Layer<T>::out_size*2];
+        }
+    }
+}
+
+template<typename T>
+T GRULayer<T>::getWVal(size_t i, size_t k) const noexcept
+{
+    T** set = zWeights.W;
+    if(k > 2 * Layer<T>::out_size)
+    {
+        k -= 2 * Layer<T>::out_size;
+        set = cWeights.W;
+    }
+    else if (k > Layer<T>::out_size)
+    {
+        k -= Layer<T>::out_size;
+        set = rWeights.W;
+    }
+
+    return set[i][k];
+}
+
+template<typename T>
+T GRULayer<T>::getUVal(size_t i, size_t k) const noexcept
+{
+    T** set = zWeights.U;
+    if(k > 2 * Layer<T>::out_size)
+    {
+        k -= 2 * Layer<T>::out_size;
+        set = cWeights.U;
+    }
+    else if (k > Layer<T>::out_size)
+    {
+        k -= Layer<T>::out_size;
+        set = rWeights.U;
+    }
+
+    return set[i][k];
+}
+
+template<typename T>
+T GRULayer<T>::getBVal(size_t i, size_t k) const noexcept
+{
+    T** set = zWeights.b;
+    if(k > 2 * Layer<T>::out_size)
+    {
+        k -= 2 * Layer<T>::out_size;
+        set = cWeights.b;
+    }
+    else if (k > Layer<T>::out_size)
+    {
+        k -= Layer<T>::out_size;
+        set = rWeights.b;
+    }
+
+    return set[i][k];
+}
+#endif // USE_EIGEN
+
+} // namespace MLUtils
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.h
@@ -0,0 +1,88 @@
+#ifndef GRU_H_INCLUDED
+#define GRU_H_INCLUDED
+
+#include <algorithm>
+#include <numeric>
+#include <cmath>
+#include <cstring>
+
+#ifdef USE_EIGEN
+#include "gru_eigen.h"
+#include "gru_eigen.cpp"
+#else
+#include "Layer.h"
+
+namespace MLUtils
+{
+
+template<typename T>
+class GRULayer : public Layer<T>
+{
+public:
+    GRULayer (size_t in_size, size_t out_size);
+    virtual ~GRULayer();
+
+    virtual void reset()
+    {
+        std::fill(ht1, ht1 + Layer<T>::out_size, (T) 0);
+    }
+
+    virtual inline void forward(const T* input, T* h) override
+    {
+        for(size_t i = 0; i < Layer<T>::out_size; ++i)
+        {
+            zVec[i] = sigmoid(vMult(zWeights.W[i], input, Layer<T>::in_size) + vMult(zWeights.U[i], ht1, Layer<T>::out_size) + zWeights.b[0][i] + zWeights.b[1][i]);
+            rVec[i] = sigmoid(vMult(rWeights.W[i], input, Layer<T>::in_size) + vMult(rWeights.U[i], ht1, Layer<T>::out_size) + rWeights.b[0][i] + rWeights.b[1][i]);
+            cVec[i] = std::tanh(vMult(cWeights.W[i], input, Layer<T>::in_size) + rVec[i] * (vMult(cWeights.U[i], ht1, Layer<T>::out_size) + cWeights.b[1][i]) + cWeights.b[0][i]);
+            h[i] = ((T) 1 - zVec[i]) * cVec[i] + zVec[i] * ht1[i];
+        }
+    
+        std::copy(h, h + Layer<T>::out_size, ht1);
+    }
+
+    inline T vMult(const T* arg1, const T* arg2, size_t dim)
+    {
+        return std::inner_product(arg1, arg1 + dim, arg2, (T) 0);
+    }
+
+    inline T sigmoid(T value)
+    {
+        return (T) 1 / ((T) 1 + std::exp(-value));
+    }
+
+    void setWVals(T** wVals);
+    void setUVals(T** uVals);
+    void setBVals(T** bVals);
+
+    T getWVal(size_t i, size_t k) const noexcept;
+    T getUVal(size_t i, size_t k) const noexcept;
+    T getBVal(size_t i, size_t k) const noexcept;
+
+protected:
+    T* ht1;
+
+    struct WeightSet
+    {
+        WeightSet (size_t in_size, size_t out_size);
+        ~WeightSet();
+
+        T** W;
+        T** U;
+        T* b[2];
+        const size_t out_size;
+    };
+
+    WeightSet zWeights;
+    WeightSet rWeights;
+    WeightSet cWeights;
+
+    T* zVec;
+    T* rVec;
+    T* cVec;
+};
+
+} // namespace MLUtils
+
+#endif // USE_EIGEN
+
+#endif // GRU_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.cpp b/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.cpp
@@ -0,0 +1,111 @@
+#ifdef USE_EIGEN
+
+#include "gru_eigen.h"
+
+namespace MLUtils
+{
+
+template<typename T>
+GRULayer<T>::GRULayer (size_t in_size, size_t out_size) :
+    Layer<T> (in_size, out_size)
+{
+    wVec_z.resize (out_size, in_size);
+    wVec_r.resize (out_size, in_size);
+    wVec_c.resize (out_size, in_size);
+    uVec_z.resize (out_size, out_size);
+    uVec_r.resize (out_size, out_size);
+    uVec_c.resize (out_size, out_size);
+    bVec_z.resize (out_size, 2);
+    bVec_r.resize (out_size, 2);
+    bVec_c.resize (out_size, 2);
+
+    ht1.resize (out_size, 1);
+    zVec.resize (out_size, 1);
+    rVec.resize (out_size, 1);
+    cVec.resize (out_size, 1);
+
+    inVec.resize (in_size, 1);
+    ones = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>::Ones (out_size, 1);
+}
+
+template<typename T>
+void GRULayer<T>::setWVals(T** wVals)
+{
+    for (size_t i = 0; i < Layer<T>::in_size; ++i)
+    {
+        for (size_t k = 0; k < Layer<T>::out_size; ++k)
+        {
+            wVec_z (k, i) = wVals[i][k];
+            wVec_r (k, i) = wVals[i][k+Layer<T>::out_size];
+            wVec_c (k, i) = wVals[i][k+Layer<T>::out_size*2];
+        }
+    }
+}
+
+template<typename T>
+void GRULayer<T>::setUVals(T** uVals)
+{
+    for (size_t i = 0; i < Layer<T>::out_size; ++i)
+    {
+        for (size_t k = 0; k < Layer<T>::out_size; ++k)
+        {
+            uVec_z (k, i) = uVals[i][k];
+            uVec_r (k, i) = uVals[i][k+Layer<T>::out_size];
+            uVec_c (k, i) = uVals[i][k+Layer<T>::out_size*2];
+        }
+    }
+}
+
+template<typename T>
+void GRULayer<T>::setBVals(T** bVals)
+{
+    for (size_t i = 0; i < 2; ++i)
+    {
+        for (size_t k = 0; k < Layer<T>::out_size; ++k)
+        {
+            bVec_z (k, i) = bVals[i][k];
+            bVec_r (k, i) = bVals[i][k+Layer<T>::out_size];
+            bVec_c (k, i) = bVals[i][k+Layer<T>::out_size*2];
+        }
+    }
+}
+
+template<typename T>
+T GRULayer<T>::getWVal(size_t i, size_t k) const noexcept
+{
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> set = wVec_z;
+    if(k > 2 * Layer<T>::out_size)
+        set = wVec_c;
+    else if (k > Layer<T>::out_size)
+        set = wVec_r;
+
+    return set (k % Layer<T>::out_size, i);
+}
+
+template<typename T>
+T GRULayer<T>::getUVal(size_t i, size_t k) const noexcept
+{
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> set = uVec_z;
+    if(k > 2 * Layer<T>::out_size)
+        set = uVec_c;
+    else if (k > Layer<T>::out_size)
+        set = uVec_r;
+
+    return set (k % Layer<T>::out_size, i);
+}
+
+template<typename T>
+T GRULayer<T>::getBVal(size_t i, size_t k) const noexcept
+{
+    Eigen::Matrix<T, Eigen::Dynamic, 2> set = bVec_z;
+    if(k > 2 * Layer<T>::out_size)
+        set = bVec_c;
+    else if (k > Layer<T>::out_size)
+        set = bVec_r;
+
+    return set (k % Layer<T>::out_size, i);
+}
+
+} // namespace MLUtils
+
+#endif // USE_EIGEN
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.h b/Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.h
@@ -0,0 +1,73 @@
+#ifndef GRUEIGEN_H_INCLUDED
+#define GRUEIGEN_H_INCLUDED
+
+#include <Eigen/Dense>
+#include "Layer.h"
+
+namespace MLUtils
+{
+
+template<typename T>
+class GRULayer : public Layer<T>
+{
+public:
+    GRULayer (size_t in_size, size_t out_size);
+    virtual ~GRULayer() {}
+
+    void reset()
+    {
+        std::fill(ht1.data(), ht1.data() + Layer<T>::out_size, (T) 0);
+    }
+
+    inline void forward(const T* input, T* h) override
+    {
+        inVec = Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>> (input, Layer<T>::in_size, 1);
+
+        zVec = wVec_z * inVec + uVec_z * ht1 + bVec_z.col (0) + bVec_z.col (1);
+        rVec = wVec_r * inVec + uVec_r * ht1 + bVec_r.col (0) + bVec_r.col (1);
+        sigmoid (zVec);
+        sigmoid (rVec);
+        
+        cVec = wVec_c * inVec + rVec.cwiseProduct (uVec_c * ht1 + bVec_c.col (1)) + bVec_c.col (0);
+        cVec = cVec.array().tanh();
+        
+        ht1 = (ones - zVec).cwiseProduct (cVec) + zVec.cwiseProduct (ht1);
+        std::copy (ht1.data(), ht1.data() + Layer<T>::out_size, h);
+    }
+
+    inline void sigmoid (Eigen::Matrix<T, Eigen::Dynamic, 1>& vector)
+    {
+        vector = (T) 1 / (((T) -1 * vector.array()).array().exp() + (T) 1);
+    }
+
+    void setWVals(T** wVals);
+    void setUVals(T** uVals);
+    void setBVals(T** bVals);
+
+    T getWVal(size_t i, size_t k) const noexcept;
+    T getUVal(size_t i, size_t k) const noexcept;
+    T getBVal(size_t i, size_t k) const noexcept;
+
+private:
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> wVec_z;
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> wVec_r;
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> wVec_c;
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> uVec_z;
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> uVec_r;
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> uVec_c;
+    Eigen::Matrix<T, Eigen::Dynamic, 2> bVec_z;
+    Eigen::Matrix<T, Eigen::Dynamic, 2> bVec_r;
+    Eigen::Matrix<T, Eigen::Dynamic, 2> bVec_c;
+
+    Eigen::Matrix<T, Eigen::Dynamic, 1> ht1;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> zVec;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> rVec;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> cVec;
+
+    Eigen::Matrix<T, Eigen::Dynamic, 1> inVec;
+    Eigen::Matrix<T, Eigen::Dynamic, 1> ones;
+};
+
+} // namespace MLUtils
+
+#endif // GRUEIGEN_H_INCLUDED
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/tests/CMakeLists.txt b/Plugin/Source/Processors/Hysteresis/RTNeural/tests/CMakeLists.txt
@@ -0,0 +1,6 @@
+include_directories(../src)
+include_directories(../modules/Eigen)
+include_directories(../modules/json)
+
+add_executable(dense dense.cpp)
+target_link_libraries(dense LINK_PUBLIC MLUtils)
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/tests/dense.cpp b/Plugin/Source/Processors/Hysteresis/RTNeural/tests/dense.cpp
@@ -0,0 +1,37 @@
+#include <iostream>
+#include <fstream>
+#include "model_loader.hpp"
+#include "load_csv.hpp"
+
+using T = double;
+
+int main()
+{
+    std::cout << "TESTING DENSE IMPLEMENTATION..." << std::endl;
+
+    std::ifstream jsonStream("models/dense.json", std::ifstream::binary);
+    auto model = json_parser::parseJson<T>(jsonStream);
+
+    std::ifstream pythonX("test_data/dense_x_python.csv");
+    auto xData = load_csv::loadFile<T>(pythonX);
+
+    std::ifstream pythonY("test_data/dense_y_python.csv");
+    const auto yRefData = load_csv::loadFile<T>(pythonY);
+
+    std::vector<T> yData (xData.size(), (T) 0);
+    for(size_t n = 0; n < xData.size(); ++n)
+        yData[n] = model->forward(xData[n].data());
+
+    constexpr T THRESH = 1.0e-6; // 2.0e-8;
+    for(size_t n = 0; n < xData.size(); ++n)
+    {
+        auto err = std::abs(yData[n] - yRefData[n][0]);
+        if(err > THRESH)
+        {
+            std::cout << "ERR: " << err << ", idx: " << n << std::endl;
+            break;
+        }
+    }
+
+    return 0;
+}
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/tests/load_csv.hpp b/Plugin/Source/Processors/Hysteresis/RTNeural/tests/load_csv.hpp
@@ -0,0 +1,32 @@
+#include <fstream>
+#include <vector>
+#include <string>
+#include <sstream>
+
+namespace load_csv {
+
+template<typename T>
+std::vector<std::vector<T>> loadFile(std::ifstream& stream)
+{
+    std::vector<std::vector<T>> vec;
+
+    std::string line;
+    if(stream.is_open()) {
+        while(std::getline(stream, line)) {
+            std::stringstream ss(line);
+
+            std::string number;
+            std::vector<T> lineVec;
+            while(std::getline(ss, number, ','))
+                lineVec.push_back(static_cast<T>(std::stod(number)));
+
+            vec.push_back(lineVec);
+        }
+            
+        stream.close();
+    }
+
+    return vec;
+}
+
+} // namespace load_csv
diff --git a/Plugin/Source/Processors/Hysteresis/RTNeural/tests/model_loader.hpp b/Plugin/Source/Processors/Hysteresis/RTNeural/tests/model_loader.hpp
@@ -0,0 +1,86 @@
+#pragma once
+
+#include <iostream>
+#include <fstream>
+#include <string>
+#include <memory>
+#include "../src/Model.h"
+#include "../modules/json/json.hpp"
+
+namespace json_parser {
+
+using json = nlohmann::json;
+
+template<typename T>
+std::unique_ptr<MLUtils::Dense<T>> createDense(size_t in_size, size_t out_size, const json& weights)
+{
+    auto dense = std::make_unique<MLUtils::Dense<T>>(in_size, out_size);
+
+    // load weights
+    std::vector<std::vector<T>> denseWeights (out_size);
+    for(auto& w : denseWeights)
+        w.resize(in_size, (T) 0);
+
+    auto layerWeights = weights[0];
+    for(size_t i = 0; i < layerWeights.size(); ++i)
+    {
+        auto lw = layerWeights[i];
+        for(size_t j = 0; j < lw.size(); ++j)
+            denseWeights[j][i] = lw[j].get<T>();
+    }
+    
+    dense->setWeights(denseWeights);
+
+    // load biases
+    std::vector<T> denseBias = weights[1].get<std::vector<T>>();
+    dense->setBias(denseBias.data());
+
+    return std::move(dense);
+}
+
+template<typename T>
+std::unique_ptr<MLUtils::Model<T>> parseJson (std::ifstream& jsonStream)
+{
+    json parent;
+    jsonStream >> parent;
+    auto shape = parent["in_shape"];
+    auto layers = parent["layers"];
+
+    if(! shape.is_array() || ! layers.is_array())
+        return {};
+
+    const auto nDims = shape.back().get<int>();
+    std::cout << "# dimensions: " << nDims << std::endl;
+
+    auto model = std::make_unique<MLUtils::Model<T>> (nDims);
+
+    for(const auto& l : layers)
+    {
+        const auto type = l["type"].get<std::string>();
+        std::cout << "Layer: " << type << std::endl;
+
+        const auto layerShape = l["shape"];
+        const auto layerDims = layerShape.back().get<int>();
+        std::cout << "  Dims: " << layerDims << std::endl;
+
+        const auto weights = l["weights"];
+
+        if(type == "dense")
+        {
+            auto dense = createDense<T>(model->getNextInSize(), layerDims, weights);
+            model->addLayer(dense.release());
+
+            const auto activationType = l["activation"].get<std::string>();
+            if(activationType == "tanh")
+            {
+                std::cout << "  activation: " << activationType << std::endl;
+                auto activation = std::make_unique<MLUtils::TanhActivation<T>> (layerDims);
+                model->addLayer(activation.release());
+            }
+        }
+    }
+
+    return std::move(model);
+}
+
+} // namespace json_parser
diff --git a/Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_0.json b/Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_0.json
@@ -0,0 +1,2627 @@
+{
+    "drive_0_0": {
+        "in_shape": [
+            null,
+            5
+        ],
+        "layers": [
+            {
+                "type": "dense",
+                "activation": "tanh",
+                "shape": [
+                    null,
+                    4
+                ],
+                "weights": [
+                    [
+                        [
+                            -0.3838985562324524,
+                            0.6514065265655518,
+                            -0.3130311667919159,
+                            -0.6012476682662964
+                        ],
+                        [
+                            -0.1512528359889984,
+                            0.8709132671356201,
+                            0.8421193957328796,
+                            -0.1209956482052803
+                        ],
+                        [
+                            0.00805120263248682,
+                            0.2968045771121979,
+                            -0.313728392124176,
+                            0.8106476664543152
+                        ],
+                        [
+                            -0.4329283535480499,
+                            0.13254141807556152,
+                            -0.1861957311630249,
+                            0.1284332573413849
+                        ],
+                        [
+                            0.37197181582450867,
+                            0.32712888717651367,
+                            0.12333491444587708,
+                            0.9722179174423218
+                        ]
+                    ],
+                    [
+                        -0.0703265443444252,
+                        0.0006420251447707415,
+                        0.0023529541213065386,
+                        0.024875136092305183
+                    ]
+                ]
+            },
+            {
+                "type": "dense",
+                "activation": "tanh",
+                "shape": [
+                    null,
+                    4
+                ],
+                "weights": [
+                    [
+                        [
+                            -0.5492371320724487,
+                            -0.11236018687486649,
+                            1.016867995262146,
+                            -0.2548956871032715
+                        ],
+                        [
+                            -0.4289317727088928,
+                            0.07445983588695526,
+                            -0.09711115062236786,
+                            0.09645948559045792
+                        ],
+                        [
+                            -0.05926113948225975,
+                            -0.41058632731437683,
+                            0.5772331357002258,
+                            0.5014914274215698
+                        ],
+                        [
+                            0.8354715704917908,
+                            0.08827757090330124,
+                            0.329765260219574,
+                            0.6428239941596985
+                        ]
+                    ],
+                    [
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diff --git a/Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_10.json b/Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_10.json
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+}
+\ No newline at end of file

	AnalogTapeModel Physical modelling signal processing for analog tape recording
	Log \| Files \| Refs \| Submodules \| README \| LICENSE

M	.travis.yml	\|	2	+-
M	Manual/ChowTapeManual.pdf	\|	0
M	Manual/ChowTapeManual.tex	\|	8	+++++++-
M	Plugin/Bench/Source/Main.cpp	\|	10	+++++++++-
M	Plugin/CHOWTapeModel.jucer	\|	46	++++++++++++++++++++++++++++++++++++++++++++--
M	Plugin/Source/Processors/Hysteresis/HysteresisProcessing.cpp	\|	24	++++++++++++++++++++++--
M	Plugin/Source/Processors/Hysteresis/HysteresisProcessing.h	\|	9	++++++++-
M	Plugin/Source/Processors/Hysteresis/HysteresisProcessor.cpp	\|	4	++--
A	Plugin/Source/Processors/Hysteresis/HysteresisSTN.cpp	\|	86	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/HysteresisSTN.h	\|	40	++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/.gitignore	\|	4	++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/CMakeLists.txt	\|	20	++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/LICENSE	\|	29	+++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/README.md	\|	15	+++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/CMakeLists.txt	\|	13	+++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.cpp	\|	173	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/Json2RnnParser.h	\|	25	+++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/Layer.h	\|	29	+++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/Model.h	\|	80	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/activation.h	\|	89	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/dense.h	\|	120	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/dense_eigen.h	\|	73	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.cpp	\|	158	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/gru.h	\|	88	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.cpp	\|	111	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/src/gru_eigen.h	\|	73	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/tests/CMakeLists.txt	\|	6	++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/tests/dense.cpp	\|	37	+++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/tests/load_csv.hpp	\|	32	++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/RTNeural/tests/model_loader.hpp	\|	86	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_0.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_10.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_100.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_20.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_30.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_40.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_50.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_60.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_70.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_80.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A	Plugin/Source/Processors/Hysteresis/STN_Models/hyst_width_90.json	\|	2628	+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++