1
0
mirror of https://github.com/f4exb/sdrangel.git synced 2024-11-29 19:28:47 -05:00
sdrangel/sdrbase/dsp/fftnr.cpp

149 lines
4.2 KiB
C++

///////////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2023 Edouard Griffiths, F4EXB <f4exb06@gmail.com> //
// //
// Helper class for noise reduction //
// //
// This program is free software; you can redistribute it and/or modify //
// it under the terms of the GNU General Public License as published by //
// the Free Software Foundation as version 3 of the License, or //
// (at your option) any later version. //
// //
// This program is distributed in the hope that it will be useful, //
// but WITHOUT ANY WARRANTY; without even the implied warranty of //
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the //
// GNU General Public License V3 for more details. //
// //
// You should have received a copy of the GNU General Public License //
// along with this program. If not, see <http://www.gnu.org/licenses/>. //
///////////////////////////////////////////////////////////////////////////////////
#include <algorithm>
#include <numeric>
#include <QDebug>
#include "fftnr.h"
FFTNoiseReduction::FFTNoiseReduction(int len) :
m_flen(len)
{
m_scheme = SchemeAverage;
m_mags = new float[m_flen];
m_tmp = new float[m_flen];
m_aboveAvgFactor = 1.0;
m_sigmaFactor = 1.0;
m_nbPeaks = m_flen;
}
FFTNoiseReduction::~FFTNoiseReduction()
{
delete[] m_mags;
delete[] m_tmp;
}
void FFTNoiseReduction::init()
{
std::fill(m_mags, m_mags + m_flen, 0);
std::fill(m_tmp, m_tmp + m_flen, 0);
m_magAvg = 0;
}
void FFTNoiseReduction::push(cmplx data, int index)
{
m_mags[index] = std::abs(data);
if ((m_scheme == SchemeAverage) || (m_scheme == SchemeAvgStdDev)) {
m_magAvg += m_mags[index];
}
}
void FFTNoiseReduction::calc()
{
if (m_scheme == SchemeAverage)
{
m_magAvg /= m_flen;
m_magAvg = m_expFilter.push(m_magAvg);
}
if (m_scheme == SchemeAvgStdDev)
{
m_magAvg /= m_flen;
auto variance_func = [this](float accumulator, const float& val) {
return accumulator + ((val - m_magAvg)*(val - m_magAvg) / (m_flen - 1));
};
float var = std::accumulate(m_mags, m_mags + m_flen, 0.0, variance_func);
m_magThr = (m_sigmaFactor/2.0)*std::sqrt(var) + m_magAvg;
m_magThr = m_expFilter.push(m_magThr);
}
else if (m_scheme == SchemePeaks)
{
std::copy(m_mags, m_mags + m_flen, m_tmp);
std::sort(m_tmp, m_tmp + m_flen);
m_magThr = m_tmp[m_flen - m_nbPeaks];
}
}
bool FFTNoiseReduction::cut(int index)
{
if (m_scheme == SchemeAverage)
{
return m_mags[index] < m_aboveAvgFactor * m_magAvg;
}
else if ((m_scheme == SchemePeaks) || (m_scheme == SchemeAvgStdDev))
{
return m_mags[index] < m_magThr;
}
return false;
}
void FFTNoiseReduction::setScheme(Scheme scheme)
{
if (m_scheme != scheme) {
m_expFilter.reset();
}
m_scheme = scheme;
}
FFTNoiseReduction::ExponentialFilter::ExponentialFilter()
{
m_alpha = 1.0;
m_init = true;
}
float FFTNoiseReduction::ExponentialFilter::push(float newValue)
{
if (m_init)
{
m_prev = newValue;
m_init = false;
}
if (m_alpha == 1.0)
{
m_prev = newValue;
return newValue;
}
else
{
float result = m_alpha*m_prev + (1.0 - m_alpha)*newValue;
m_prev = result;
return result;
}
}
void FFTNoiseReduction::ExponentialFilter::reset()
{
m_init = true;
}
void FFTNoiseReduction::ExponentialFilter::setAlpha(float alpha)
{
m_alpha = alpha < 0.0f ? 0.0f : alpha > 1.0f ? 1.0f : alpha;
qDebug("FFTNoiseReduction::ExponentialFilter::setAlpha: %f", m_alpha);
m_init = true;
}