[Analyzer] use original frequency analyzer directly

This commit is contained in:
thibaud keller 2024-10-29 23:32:02 +00:00
parent 1951ec07c8
commit 29283ede63
6 changed files with 32 additions and 288 deletions

View file

@ -1,249 +1,14 @@
#include "Amuencha.hpp"
#include "sse_mathfun.h"
#include <boost/math/constants/constants.hpp>
#include <boost/math/special_functions/bessel.hpp>
#include <sys/stat.h>
#include <iostream>
#include <fstream>
namespace Amuencha
{
void Analyser::prepare(halp::setup info)
void Model::prepare(halp::setup info)
{
sampling_rate = info.rate;
samplerate_div_2pi = sampling_rate/two_pi;
}
void Analyser::operator()(halp::tick t)
void Model::operator()(halp::tick t)
{
using namespace std;
if (chunks.empty())
chunks.emplace_back(make_pair(inputs.audio[0], t.frames));
else
chunks[0] = make_pair(inputs.audio[0], t.frames);
int new_data_size = 0;
for (auto c: chunks) new_data_size += c.second;
// shift the old data to make room for the new
int new_data_pos = big_buffer.size() - new_data_size;
//cout << "data " << new_data_size << " " << big_buffer.size() << endl;
// std::copy can cope with overlapping regions in this copy direction
if (new_data_pos > 0)
copy(big_buffer.begin() + new_data_size,
big_buffer.end(),
big_buffer.begin());
// now copy each chunk at its position in the big buffer
for (auto c: chunks)
{
if (new_data_pos < 0)
{
// discard too old chunks
if (c.second <= -new_data_pos)
{
new_data_pos += c.second;
continue;
}
// partial copy of chunks that fall on the edge
copy(c.first + new_data_pos, c.first + c.second, &big_buffer[0]);
new_data_pos = c.second + new_data_pos;
continue;
}
copy(c.first, c.first + c.second, &big_buffer[new_data_pos]);
new_data_pos += c.second;
}
// Apply the filter bank
float *bbend = &big_buffer[0] + big_buffer.size();
for (int idx{0}; idx < frequencies.size(); ++idx)
{
const auto& ws = windowed_sines[idx];
int wsize = ws.size();
float* sig = bbend - wsize;
v4sf acc = {0.f, 0.f, 0.f, 0.f};
for (int i{0}; i < wsize; ++i) acc += ws[i] * sig[i];
float norm = acc[0] * acc[0] + acc[1]*acc[1];
float reassign = frequencies[idx];
if (norm > 0)
reassign -= (acc[0] * acc[3] - acc[1] * acc[2]) * samplerate_div_2pi / norm;
reassigned_frequencies[idx] = reassign;
power_spectrum[idx] = norm * power_normalization_factors[idx];
}
send_message({.min = inputs.min,
.max = inputs.max,
.reassigned_frequencies = this->reassigned_frequencies,
.power_spectrum = this->power_spectrum});
}
void Analyser::analyzer_setup(float max_buffer_duration)
{
using namespace boost::math::float_constants;
using namespace std;
// Prepare the windows
//std::vector<std::vector<v4sf>> windowed_sines;
windowed_sines.resize(frequencies.size());
power_normalization_factors.resize(frequencies.size());
int big_buffer_size = 0;
for (int idx{0}; idx < frequencies.size(); ++idx)
{
// for each freq, span at least 20 periods for more precise measurements
// This still gives reasonable latencies, e.g. 50ms at 400Hz, 100ms at 200Hz, 400ms at 50Hz...
// Could also span more for even better measurements, with larger
// computation cost and latency
float f = frequencies[idx];
int window_size = (int)(min(inputs.periods / f, max_buffer_duration * 0.001f)
* sampling_rate);
vector<float> window(window_size);
vector<float> window_deriv(window_size);
if (!read_from_cache(window, window_deriv))
{
initialize_window(window);
initialize_window_deriv(window_deriv);
write_to_cache(window, window_deriv);
}
windowed_sines[idx].resize(window_size);
float wsum = 0;
for (int i{0}; i < window_size;)
{
if (i < window_size - 4)
{
v4sf tfs = {
(float)(i - window_size - 1) / sampling_rate,
(float)(i + 1 - window_size - 1) / sampling_rate,
(float)(i + 2 - window_size - 1) / sampling_rate,
(float)(i + 3 - window_size - 1) / sampling_rate
};
tfs *= (float)(-two_pi*f);
v4sf sin_tf, cos_tf;
sincos_ps(tfs, &sin_tf, &cos_tf);
for (int j{0}; j < 3; ++j)
{
v4sf ws = {
cos_tf[j] * window[i + j],
sin_tf[j] * window[i + j],
cos_tf[j] * window_deriv[i + j],
sin_tf[j] * window_deriv[i + j]
};
windowed_sines[idx][i + j] = ws;
wsum += window[i + j];
}
i += 4;
continue;
}
float t = (float)(i - window_size - 1) / sampling_rate;
float re = cosf(-two_pi * t * f);
float im = sinf(-two_pi * t * f);
v4sf ws = {
re * window[i],
im * window[i],
re * window_deriv[i],
im * window_deriv[i]
};
windowed_sines[idx][i] = ws;
wsum += window[i];
++i;
}
power_normalization_factors[idx] = 1. / (wsum * wsum);
big_buffer_size = max(big_buffer_size, window_size);
}
big_buffer.clear();
// fill with 0 signal content to start with
big_buffer.resize(big_buffer_size, 0.f);
}
void Analyser::initialize_window(std::vector<float>& window)
{
// Kaiser window with a parameter of alpha=3 that nullifies the window on edges
int size = window.size();
const float two_over_N = 2. / size;
const float alpha = 3.;
const float alpha_pi = alpha * pi;
const float inv_denom = 1. / boost::math::cyl_bessel_i(0., alpha_pi);
for (int i{0}; i < size; ++i)
{
float p = i * two_over_N - 1.;
window[i] = boost::math::cyl_bessel_i(0., alpha_pi * sqrt(1. - p * p)) * inv_denom;
}
}
void Analyser::initialize_window_deriv(std::vector<float>& window)
{
// Derivative of the Kaiser window with a parameter of alpha=3 that nullifies the window on edges
int size = window.size();
const float two_over_N = 2. / size;
const float alpha = 3.;
const float alpha_pi = alpha * pi;
const float inv_denom = 1. / boost::math::cyl_bessel_i(0., alpha_pi);
for (int i{1}; i < size; ++i)
{
float p = i * two_over_N - 1.;
window[i] = boost::math::cyl_bessel_i(1., alpha_pi * sqrt(1. - p * p)) *
inv_denom * alpha_pi / sqrt(1. - p * p) * (-p) * two_over_N;
}
// lim I1(x)/x as x->0 = 1/2
window[0] = 0.5 * inv_denom * alpha_pi * alpha_pi * two_over_N;
}
bool Analyser::read_from_cache(std::vector<float> &window, std::vector<float> &window_deriv)
{
auto it = mem_win_cache.find(window.size());
if (it != mem_win_cache.end())
{
window = it->second;
auto itd = mem_winderiv_cache.find(window.size());
if (itd != mem_winderiv_cache.end())
{
window_deriv = itd->second;
return true;
}
// else, load from disk
}
using namespace std;
// TODO: make the cache location parametrizable (and an option to not use it)
ifstream file(".amuencha_cache/w"+to_string(window.size())+".bin", ios::binary);
if (file.is_open())
{
file.read(reinterpret_cast<char*>(&window[0]), window.size()*sizeof(float));
file.read(reinterpret_cast<char*>(&window_deriv[0]), window_deriv.size()*sizeof(float));
if (file.tellg() != (window.size() + window_deriv.size()) * sizeof(float))
{
cerr << "Error: invalid cache .amuencha_cache/w"+to_string(window.size())+".bin\n";
return false;
}
return true;
}
return false;
}
void Analyser::write_to_cache(std::vector<float> &window, std::vector<float> &window_deriv)
{
using namespace std;
#if defined(_WIN32) || defined(_WIN64)
mkdir(".amuencha_cache");
#else
mkdir(".amuencha_cache", 0755);
#endif
ofstream file(".amuencha_cache/w"+to_string(window.size())+".bin", ios::binary|ios::trunc);
file.write(reinterpret_cast<char*>(&window[0]), window.size()*sizeof(float));
file.write(reinterpret_cast<char*>(&window_deriv[0]), window_deriv.size()*sizeof(float));
mem_win_cache[window.size()] = window;
mem_winderiv_cache[window.size()] = window_deriv;
analyzer.new_data(inputs.audio[0], t.frames);
}
}

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@ -5,9 +5,11 @@
#include <halp/controls.hpp>
#include <halp/meta.hpp>
#include "FrequencyAnalyzer.hpp"
namespace Amuencha
{
class Analyser
class Model
{
public:
halp_meta(name, "Amuencha")
@ -33,16 +35,16 @@ public:
halp::fixed_audio_bus<"Input", float, 1> audio;
struct : halp::hslider_i32<"Min", halp::range{.min = 0, .max = 127, .init = 24}>
{
void update(Analyser& self)
void update(Model& self)
{
self.send_message({.min = this->value, .max = self.inputs.max.value});
self.send_message({.min = this->value, .max = self.inputs.max});
}
} min;
struct : halp::hslider_i32<"Min", halp::range{.min = 0, .max = 127, .init = 72}>
{
void update(Analyser& self)
void update(Model& self)
{
self.send_message({.min = self.inputs.min.value, .max = this->value});
self.send_message({.min = self.inputs.min, .max = this->value});
}
} max;
halp::spinbox_i32<"Periods",
@ -51,10 +53,19 @@ public:
void process_message(const std::vector<float>& frequencies)
{
this->frequencies = frequencies;
reassigned_frequencies = frequencies;
power_spectrum.resize(frequencies.size());
analyzer_setup();
analyzer.setup(sampling_rate,
frequencies,
[&] (const std::vector<float>& r_f,
const std::vector<float>& p_s)
{
send_message({.min = inputs.min,
.max = inputs.max,
.reassigned_frequencies = r_f,
.power_spectrum = p_s});
},
inputs.periods);
analyzer.start(QThread::NormalPriority);
}
struct outs
@ -74,41 +85,9 @@ public:
// UI is defined in another file to keep things clear.
struct ui;
// max_buffer_duration in milliseconds, specifies the largest buffer for computing the frequency content
// At lower frequencies, long buffers are needed for accurate frequency separation.
// When that max buffer duration is reached, then it is capped and the frequency resolution decreases
// Too low buffers also limit the min_freq, duration must be >= period
void analyzer_setup(float max_buffer_duration = 500);
private:
// new data chunks arrived since the last periodic processing
std::vector<std::pair<float*,int>> chunks;
// The window is the usual Kaiser with alpha=3
static void initialize_window(std::vector<float>& window);
static void initialize_window_deriv(std::vector<float>& window);
// The filter bank. One filter per frequency
// The 4 entries in the v4sf are the real, imaginary parts of the windowed
// sine wavelet, and the real, imaginary parts of the derived windowed sine
// used for reassigning the power spectrum.
// Hopefully, with SIMD, computing all 4 of them is the same price as just one
// TODO: v8sf and compute 2 freqs at the same time
typedef float v4sf __attribute__ ((vector_size (16)));
std::vector<std::vector<v4sf>> windowed_sines;
std::vector<float> frequencies;
std::vector<float> power_normalization_factors;
float sampling_rate;
float samplerate_div_2pi;
std::vector<float> big_buffer;
std::vector<float> reassigned_frequencies;
std::vector<float> power_spectrum;
// caching computations for faster init
// on disk for persistence between executions,
// in memory for avoiding reloading from disk when changing the spiral size
bool read_from_cache(std::vector<float>& window, std::vector<float>& window_deriv);
void write_to_cache(std::vector<float>& window, std::vector<float>& window_deriv);
std::map<int, std::vector<float>> mem_win_cache, mem_winderiv_cache;
FrequencyAnalyzer analyzer;
};
}

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@ -1,12 +1,13 @@
#pragma once
#include <Amuencha/AmuenchaModel.hpp>
#include <halp/layout.hpp>
#include <Amuencha/AmuenchaModel.hpp>
#include "SpiralDisplay.hpp"
namespace Amuencha
{
struct Analyser::ui
struct Model::ui
{
using enum halp::colors;
using enum halp::layouts;
@ -43,7 +44,7 @@ struct Analyser::ui
// Receive a message on the UI thread from the processing thread
static void process_message(ui& self, const processor_to_ui& msg)
{
self.spiral.set_min_max_notes(msg.min, msg.max);
// self.spiral.set_min_max_notes(msg.min, msg.max);
if (msg.power_spectrum.empty() ||
msg.reassigned_frequencies.empty())

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@ -94,7 +94,7 @@ void Amuencha::SpiralDisplay::compute_frequencies()
void Amuencha::SpiralDisplay::power_handler(const std::vector<float>& reassigned_frequencies,
const std::vector<float>& power_spectrum)
{
// fill(display_spectrum.begin(), display_spectrum.end(), 0.);
fill(display_spectrum.begin(), display_spectrum.end(), 0.);
// simple histogram-like sum, assuming power entries are normalized
int nidx = reassigned_frequencies.size();

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@ -30,8 +30,6 @@ struct SpiralDisplay
if (display_bins.empty())
compute_frequencies();
ctx.set_stroke_color({0, 0, 0, 255});
for (int i{0}; i < 12; i++)
{
ctx.move_to(half, half);

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@ -23,9 +23,10 @@ avnd_score_plugin_add(
Amuencha/AmuenchaUi.hpp
Amuencha/SpiralDisplay.hpp
Amuencha/SpiralDisplay.cpp
Amuencha/sse_mathfun.h
Amuencha/FrequencyAnalyzer.hpp
Amuencha/FrequencyAnalyzer.cpp
TARGET amuencha
MAIN_CLASS Analyser
MAIN_CLASS Model
NAMESPACE Amuencha
)