kopia lustrzana https://github.com/f4exb/sdrangel
149 wiersze
4.2 KiB
C++
149 wiersze
4.2 KiB
C++
///////////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2023 Edouard Griffiths, F4EXB <f4exb06@gmail.com> //
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// //
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// Helper class for noise reduction //
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// //
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// This program is free software; you can redistribute it and/or modify //
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// it under the terms of the GNU General Public License as published by //
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// the Free Software Foundation as version 3 of the License, or //
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// (at your option) any later version. //
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// //
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// This program is distributed in the hope that it will be useful, //
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// but WITHOUT ANY WARRANTY; without even the implied warranty of //
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the //
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// GNU General Public License V3 for more details. //
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// //
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// You should have received a copy of the GNU General Public License //
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// along with this program. If not, see <http://www.gnu.org/licenses/>. //
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///////////////////////////////////////////////////////////////////////////////////
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#include <algorithm>
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#include <numeric>
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#include <QDebug>
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#include "fftnr.h"
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FFTNoiseReduction::FFTNoiseReduction(int len) :
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m_flen(len)
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{
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m_scheme = SchemeAverage;
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m_mags = new float[m_flen];
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m_tmp = new float[m_flen];
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m_aboveAvgFactor = 1.0;
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m_sigmaFactor = 1.0;
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m_nbPeaks = m_flen;
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}
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FFTNoiseReduction::~FFTNoiseReduction()
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{
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delete[] m_mags;
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delete[] m_tmp;
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}
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void FFTNoiseReduction::init()
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{
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std::fill(m_mags, m_mags + m_flen, 0);
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std::fill(m_tmp, m_tmp + m_flen, 0);
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m_magAvg = 0;
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}
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void FFTNoiseReduction::push(cmplx data, int index)
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{
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m_mags[index] = std::abs(data);
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if ((m_scheme == SchemeAverage) || (m_scheme == SchemeAvgStdDev)) {
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m_magAvg += m_mags[index];
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}
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}
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void FFTNoiseReduction::calc()
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{
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if (m_scheme == SchemeAverage)
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{
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m_magAvg /= m_flen;
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m_magAvg = m_expFilter.push(m_magAvg);
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}
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if (m_scheme == SchemeAvgStdDev)
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{
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m_magAvg /= m_flen;
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auto variance_func = [this](float accumulator, const float& val) {
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return accumulator + ((val - m_magAvg)*(val - m_magAvg) / (m_flen - 1));
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};
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float var = std::accumulate(m_mags, m_mags + m_flen, 0.0, variance_func);
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m_magThr = (m_sigmaFactor/2.0)*std::sqrt(var) + m_magAvg;
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m_magThr = m_expFilter.push(m_magThr);
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}
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else if (m_scheme == SchemePeaks)
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{
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std::copy(m_mags, m_mags + m_flen, m_tmp);
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std::sort(m_tmp, m_tmp + m_flen);
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m_magThr = m_tmp[m_flen - m_nbPeaks];
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}
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}
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bool FFTNoiseReduction::cut(int index)
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{
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if (m_scheme == SchemeAverage)
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{
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return m_mags[index] < m_aboveAvgFactor * m_magAvg;
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}
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else if ((m_scheme == SchemePeaks) || (m_scheme == SchemeAvgStdDev))
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{
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return m_mags[index] < m_magThr;
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}
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return false;
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}
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void FFTNoiseReduction::setScheme(Scheme scheme)
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{
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if (m_scheme != scheme) {
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m_expFilter.reset();
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}
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m_scheme = scheme;
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}
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FFTNoiseReduction::ExponentialFilter::ExponentialFilter()
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{
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m_alpha = 1.0;
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m_init = true;
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}
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float FFTNoiseReduction::ExponentialFilter::push(float newValue)
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{
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if (m_init)
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{
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m_prev = newValue;
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m_init = false;
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}
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if (m_alpha == 1.0)
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{
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m_prev = newValue;
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return newValue;
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}
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else
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{
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float result = m_alpha*m_prev + (1.0 - m_alpha)*newValue;
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m_prev = result;
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return result;
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}
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}
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void FFTNoiseReduction::ExponentialFilter::reset()
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{
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m_init = true;
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}
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void FFTNoiseReduction::ExponentialFilter::setAlpha(float alpha)
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{
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m_alpha = alpha < 0.0f ? 0.0f : alpha > 1.0f ? 1.0f : alpha;
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qDebug("FFTNoiseReduction::ExponentialFilter::setAlpha: %f", m_alpha);
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m_init = true;
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}
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