-
Notifications
You must be signed in to change notification settings - Fork 7
/
LMSNoisereducer.cpp
145 lines (123 loc) · 3.33 KB
/
LMSNoisereducer.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
#include "LMSNoisereducer.h"
LMSNoisereducer::LMSNoisereducer(){
q = nullptr;
q = eqlms_cccf_create(NULL, p);
eqlms_cccf_set_bw(q, mu);
eqlms_cccf_print(q);
first = false;
for (int i; i < 200; i++)
{
samples_training.push_back(0);
}
}
LMSNoisereducer::~LMSNoisereducer()
{
eqlms_cccf_destroy(q);
}
void LMSNoisereducer::Process(const IQSampleVector &samples_in, IQSampleVector &samples_out)
{
unsigned int i = 0;
/*if (samples_training.size() < samples_in.size())
{
samples_training.clear();
for (auto con : samples_in)
{
samples_training.push_back(con);
}
}
*/
for (auto con : samples_in)
{
IQSample s;
// push input sample
eqlms_cccf_push(q, con);
samples_training.push_back(con);
// compute output sample
eqlms_cccf_execute(q, &s);
samples_out.push_back(s);
// update internal weights
s = samples_training.front();
samples_training.pop_front();
eqlms_cccf_step(q, con, s);
//eqlms_cccf_step(q, samples_training[i], s);
//eqlms_cccf_step_blind(q, s);
//samples_training[i++] = con;
}
}
LMSNoiseReduction::LMSNoiseReduction(int LMS_nr_strength)
{
//LMS_Norm_instance.numTaps = calc_taps;
//LMS_Norm_instance.pCoeffs = LMS_NormCoeff_f32;
//LMS_Norm_instance.pState = LMS_StateF32;
// Calculate "mu" (convergence rate) from user "DSP Strength" setting. This needs to be significantly de-linearized to
// squeeze a wide range of adjustment (e.g. several magnitudes) into a fairly small numerical range.
mu_calc = LMS_nr_strength; // get user setting
// New DSP NR "mu" calculation method as of 0.0.214
mu_calc /= 2; // scale input value
mu_calc += 2; // offset zero value
mu_calc /= 10; // convert from "bels" to "deci-bels"
mu_calc = powf(10, mu_calc); // convert to ratio
mu_calc = 1 / mu_calc; // invert to fraction
//LMS_Norm_instance.mu = mu_calc;
}
Xanr::Xanr()
{
}
Xanr::~Xanr()
{
}
void Xanr::Process(const SampleVector &samples_in, SampleVector &samples_out)
{
int idx;
float c0, c1;
float y, error, sigma, inv_sigp;
float nel, nev;
for (int i = 0; i < samples_in.size(); i++)
{
ANR_d[ANR_in_idx] = samples_in[i];
y = 0;
sigma = 0;
for (int j = 0; j < ANR_taps; j++)
{
idx = (ANR_in_idx + j + ANR_delay) & ANR_mask;
y += ANR_w[j] * ANR_d[idx];
sigma += ANR_d[idx] * ANR_d[idx];
}
inv_sigp = 1.0 / (sigma + 1e-10);
error = ANR_d[ANR_in_idx] - y;
if (ANR_notch)
{
//complex<float> q;
//q.real(samples_in[i].real());
//q.imag(error);
samples_out.push_back(error);
}
else
{
//complex<float> q;
//q.real(samples_in[i].real());
//q.imag(y);
samples_out.push_back(y);
}
if ((nel = error * (1.0 - ANR_two_mu * sigma * inv_sigp)) < 0.0)
nel = -nel;
if ((nev = ANR_d[ANR_in_idx] - (1.0 - ANR_two_mu * ANR_ngamma) * y - ANR_two_mu * error * sigma * inv_sigp) < 0.0)
nev = -nev;
if (nev < nel)
{
if ((ANR_lidx += ANR_lincr) > ANR_lidx_max)
ANR_lidx = ANR_lidx_max;
else if ((ANR_lidx -= ANR_ldecr) < ANR_lidx_min)
ANR_lidx = ANR_lidx_min;
}
ANR_ngamma = ANR_gamma * (ANR_lidx * ANR_lidx) * (ANR_lidx * ANR_lidx) * ANR_den_mult;
c0 = 1.0 - ANR_two_mu * ANR_ngamma;
c1 = ANR_two_mu * error * inv_sigp;
for (int j = 0; j < ANR_taps; j++)
{
idx = (ANR_in_idx + j + ANR_delay) & ANR_mask;
ANR_w[j] = c0 * ANR_w[j] + c1 * ANR_d[idx];
}
ANR_in_idx = (ANR_in_idx + ANR_mask) & ANR_mask;
}
}