DPL2Decoder: Prefer std::vector to calloc-ed array
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71dc810418
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@ -10,8 +10,8 @@
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#include <algorithm>
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#include <algorithm>
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#include <cmath>
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#include <cmath>
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#include <cstdlib>
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#include <cstdlib>
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#include <cstring>
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#include <functional>
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#include <functional>
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#include <string.h>
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#include <vector>
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#include <vector>
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#include "AudioCommon/DPL2Decoder.h"
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#include "AudioCommon/DPL2Decoder.h"
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@ -35,11 +35,11 @@ static float adapt_l_gain, adapt_r_gain, adapt_lpr_gain, adapt_lmr_gain;
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static std::vector<float> lf, rf, lr, rr, cf, cr;
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static std::vector<float> lf, rf, lr, rr, cf, cr;
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static float LFE_buf[256];
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static float LFE_buf[256];
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static unsigned int lfe_pos;
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static unsigned int lfe_pos;
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static float* filter_coefs_lfe;
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static std::vector<float> filter_coefs_lfe;
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static unsigned int len125;
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static unsigned int len125;
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template <class T, class _ftype_t>
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template <class T>
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static _ftype_t DotProduct(int count, const T* buf, const _ftype_t* coefficients)
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static float DotProduct(int count, const T* buf, const std::vector<float>& coeffs, int offset)
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{
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{
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int i;
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int i;
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float sum0 = 0.0f, sum1 = 0.0f, sum2 = 0.0f, sum3 = 0.0f;
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float sum0 = 0.0f, sum1 = 0.0f, sum2 = 0.0f, sum3 = 0.0f;
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@ -47,21 +47,21 @@ static _ftype_t DotProduct(int count, const T* buf, const _ftype_t* coefficients
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// Unrolled loop
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// Unrolled loop
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for (i = 0; (i + 3) < count; i += 4)
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for (i = 0; (i + 3) < count; i += 4)
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{
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{
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sum0 += buf[i + 0] * coefficients[i + 0];
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sum0 += buf[i + 0] * coeffs[offset + i + 0];
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sum1 += buf[i + 1] * coefficients[i + 1];
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sum1 += buf[i + 1] * coeffs[offset + i + 1];
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sum2 += buf[i + 2] * coefficients[i + 2];
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sum2 += buf[i + 2] * coeffs[offset + i + 2];
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sum3 += buf[i + 3] * coefficients[i + 3];
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sum3 += buf[i + 3] * coeffs[offset + i + 3];
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}
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}
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// Epilogue of unrolled loop
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// Epilogue of unrolled loop
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for (; i < count; i++)
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for (; i < count; i++)
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sum0 += buf[i] * coefficients[i];
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sum0 += buf[i] * coeffs[offset + i];
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return sum0 + sum1 + sum2 + sum3;
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return sum0 + sum1 + sum2 + sum3;
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}
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}
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template <class T>
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template <class T>
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static T FIRFilter(const T* buf, int pos, int len, int count, const float* coefficients)
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static T FIRFilter(const T* buf, int pos, int len, int count, const std::vector<float>& coeffs)
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{
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{
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int count1, count2;
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int count1, count2;
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@ -81,9 +81,8 @@ static T FIRFilter(const T* buf, int pos, int len, int count, const float* coeff
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// high part of window
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// high part of window
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const T* ptr = &buf[pos];
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const T* ptr = &buf[pos];
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float r1 = DotProduct(count1, ptr, coefficients);
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float r1 = DotProduct(count1, ptr, coeffs, 0);
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coefficients += count1;
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float r2 = DotProduct(count2, buf, coeffs, count1);
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float r2 = DotProduct(count2, buf, coefficients);
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return T(r1 + r2);
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return T(r1 + r2);
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}
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}
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@ -94,15 +93,19 @@ static T FIRFilter(const T* buf, int pos, int len, int count, const float* coeff
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// N-1
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// N-1
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//
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//
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// n window length
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// n window length
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// w buffer for the window parameters
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// returns buffer with the window parameters
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*/
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*/
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static void Hamming(int n, float* w)
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static std::vector<float> Hamming(int n)
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{
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{
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std::vector<float> w(n);
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float k = float(2 * M_PI / ((float)(n - 1))); // 2*pi/(N-1)
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float k = float(2 * M_PI / ((float)(n - 1))); // 2*pi/(N-1)
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// Calculate window coefficients
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// Calculate window coefficients
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for (int i = 0; i < n; i++)
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for (int i = 0; i < n; i++)
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*w++ = float(0.54 - 0.46 * cos(k * (float)i));
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w[i] = float(0.54 - 0.46 * cos(k * (float)i));
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return w;
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}
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}
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// FIR filter design
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// FIR filter design
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@ -110,7 +113,6 @@ static void Hamming(int n, float* w)
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/* Design FIR filter using the Window method
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/* Design FIR filter using the Window method
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n filter length must be odd for HP and BS filters
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n filter length must be odd for HP and BS filters
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w buffer for the filter taps (must be n long)
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fc cutoff frequencies (1 for LP and HP, 2 for BP and BS)
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fc cutoff frequencies (1 for LP and HP, 2 for BP and BS)
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0 < fc < 1 where 1 <=> Fs/2
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0 < fc < 1 where 1 <=> Fs/2
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flags window and filter type as defined in filter.h
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flags window and filter type as defined in filter.h
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@ -118,9 +120,9 @@ variables are ored together: i.e. LP|HAMMING will give a
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low pass filter designed using a hamming window
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low pass filter designed using a hamming window
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opt beta constant used only when designing using kaiser windows
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opt beta constant used only when designing using kaiser windows
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returns 0 if OK, -1 if fail
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returns buffer for the filter taps (will be n long)
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*/
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*/
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static float* DesignFIR(unsigned int n, float fc, float opt)
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static std::vector<float> DesignFIR(unsigned int n, float fc, float opt)
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{
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{
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const unsigned int o = n & 1; // Indicator for odd filter length
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const unsigned int o = n & 1; // Indicator for odd filter length
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const unsigned int end = ((n + 1) >> 1) - o; // Loop end
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const unsigned int end = ((n + 1) >> 1) - o; // Loop end
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@ -134,12 +136,10 @@ static float* DesignFIR(unsigned int n, float fc, float opt)
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// Sanity check
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// Sanity check
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if (n == 0)
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if (n == 0)
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return nullptr;
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return {};
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float* w = (float*)calloc(sizeof(float), n);
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// Get window coefficients
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// Get window coefficients
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Hamming(n, w);
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std::vector<float> w = Hamming(n);
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// Low pass filter
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// Low pass filter
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@ -190,19 +190,14 @@ static void Done()
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{
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{
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OnSeek();
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OnSeek();
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if (filter_coefs_lfe)
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filter_coefs_lfe.clear();
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{
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free(filter_coefs_lfe);
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}
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}
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filter_coefs_lfe = nullptr;
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static std::vector<float> CalculateCoefficients125HzLowpass(int rate)
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}
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static float* CalculateCoefficients125HzLowpass(int rate)
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{
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{
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len125 = 256;
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len125 = 256;
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float f = 125.0f / (rate / 2);
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float f = 125.0f / (rate / 2);
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float* coeffs = DesignFIR(len125, f, 0);
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std::vector<float> coeffs = DesignFIR(len125, f, 0);
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static const float M3_01DB = 0.7071067812f;
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static const float M3_01DB = 0.7071067812f;
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for (unsigned int i = 0; i < len125; i++)
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for (unsigned int i = 0; i < len125; i++)
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{
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{
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@ -380,5 +375,5 @@ void DPL2Reset()
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{
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{
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olddelay = -1;
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olddelay = -1;
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oldfreq = 0;
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oldfreq = 0;
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filter_coefs_lfe = nullptr;
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filter_coefs_lfe.clear();
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}
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}
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