Merge pull request #13 from grahamwhaley/20220511_spectral_nr

Add Spectral Noise Reduction
pull/16/head
Bob Larkin 3 years ago committed by GitHub
commit 46160d8723
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
  1. 342
      AudioSpectralDenoise_F32.cpp
  2. 198
      AudioSpectralDenoise_F32.h
  3. 1
      OpenAudio_ArduinoLibrary.h
  4. 109
      docs/index.html
  5. 124
      examples/SpectralDenoise/SpectralDenoise.ino

@ -0,0 +1,342 @@
/* AudioSpectralDenoise_F2.h
* Spectral noise reduction
*
* Extracted and based on the work found in the:
* - Convolution SDR: https://github.com/DD4WH/Teensy-ConvolutionSDR
* - UHSDR: https://github.com/df8oe/UHSDR/blob/active-devel/mchf-eclipse/drivers/audio/audio_nr.c
*
* License: GNU GPLv3
* Both the Convolution SDR and UHSDR are licensed under GPLv3.
*/
#include "AudioSpectralDenoise_F32.h"
#include <new>
// No serial debug by default
static const bool serial_debug = false;
int AudioSpectralDenoise_F32::setup(const AudioSettings_F32 & settings,
const int _N_FFT)
{
enable(false); //Disable us, just incase we are already active...
sample_rate_Hz = settings.sample_rate_Hz;
if (N_FFT == -1) {
//setup the FFT and IFFT. If they return a negative FFT, it wasn't an allowed FFT size.
N_FFT = myFFT.setup(settings, _N_FFT); //hopefully, we got the same N_FFT that we asked for
if (N_FFT < 1)
return N_FFT;
N_FFT = myIFFT.setup(settings, _N_FFT); //hopefully, we got the same N_FFT that we asked for
if (N_FFT < 1)
return N_FFT;
//As we do a complex fft on a real signal, we only use half the returned FFT bins due
// to conjugate symmetry. Store the number of bins to make it obvious and handy.
N_bins = N_FFT / 2;
//Spectral uses sqrtHann filtering
(myFFT.getFFTObject())->useHanningWindow(); //applied prior to FFT
//allocate memory to hold frequency domain data - complex r+i, so double the size of the
// fft size.
complex_2N_buffer = new (std::nothrow) float32_t[2 * N_FFT];
if (complex_2N_buffer == NULL) return -1;
NR_X = new (std::nothrow) float32_t[N_bins];
if (NR_X == NULL) return -1;
ph1y = new (std::nothrow) float32_t[N_bins];
if (ph1y == NULL) return -1;
pslp = new (std::nothrow) float32_t[N_bins];
if (pslp == NULL) return -1;
xt = new (std::nothrow) float32_t[N_bins];
if (xt == NULL) return -1;
NR_SNR_post = new (std::nothrow) float32_t[N_bins];
if (NR_SNR_post == NULL) return -1;
NR_SNR_prio = new (std::nothrow) float32_t[N_bins];
if (NR_SNR_prio == NULL) return -1;
NR_Hk_old = new (std::nothrow) float32_t[N_bins];
if (NR_Hk_old == NULL) return -1;
NR_G = new (std::nothrow) float32_t[N_bins];
if (NR_G == NULL) return -1;
NR_Nest = new (std::nothrow) float32_t[N_bins];
if (NR_Nest == NULL) return -1;
}
//Clear out and initialise
for (int bindx = 0; bindx < N_bins; bindx++) {
NR_Hk_old[bindx] = 0.1; // old gain
NR_Nest[bindx] = 0.01;
NR_X[bindx] = 0.0;
NR_SNR_post[bindx] = 2.0;
NR_SNR_prio[bindx] = 1.0;
NR_G[bindx] = 0.0;
}
//Work out the 'bin' range for our chosen voice frequencies
// divide 2 to account for nyquist
VAD_low = VAD_low_freq / ((sample_rate_Hz / 2.0) / (float32_t) (N_bins));
VAD_high = VAD_high_freq / ((sample_rate_Hz / 2.0) / (float32_t) N_bins);
xih1 = powf(10, asnr / 10.0);
pfac = (1.0 / pspri - 1.0) * (1.0 + xih1);
xih1r = 1.0 / (1.0 + xih1) - 1.0;
//Configure the other things that might rely on the fft size of bitrate
tinc = 1.0 / (sample_rate_Hz / AUDIO_BLOCK_SAMPLES); //Frame time
tax = -tinc / log(tax_factor); //noise output smoothing constant in seconds = -tinc/ln(0.8)
tap = -tinc / log(tap_factor); //speech prob smoothing constant in seconds = -tinc/ln(0.9)
ap = expf(-tinc / tap); //noise output smoothing factor
ax = expf(-tinc / tax); //noise output smoothing factor
if (serial_debug) {
Serial.println(" Spectral setup with fft:" + String(N_FFT));
Serial.println(" FFT nblocks:" + String(myFFT.getNBuffBlocks()));
Serial.println(" iFFT nblocks:" + String(myIFFT.getNBuffBlocks()));
Serial.println(" Sample rate:" + String(sample_rate_Hz));
Serial.println(" bins:" + String(N_bins));
Serial.println(" VAD low:" + String(VAD_low));
Serial.println(" VAD low freq:" + String(getVADLowFreq()));
Serial.println(" VAD high:" + String(VAD_high));
Serial.println(" VAD high freq:" + String(getVADHighFreq()));
Serial.println(" tinc:" + String(tinc, 5));
Serial.println(" tax_factor:" + String(tax_factor, 5));
Serial.println(" tap_factor:" + String(tap_factor, 5));
Serial.println(" tax:" + String(tax, 5));
Serial.println(" tap:" + String(tap, 5));
Serial.println(" ax:" + String(ax, 5));
Serial.println(" ap:" + String(ap, 5));
Serial.println(" xih1:" + String(xih1, 5));
Serial.println(" xih1r:" + String(xih1r, 5));
Serial.println(" pfac:" + String(pfac, 5));
Serial.println(" snr_prio_min:" + String(getSNRPrioMin(), 5));
Serial.println(" power_threshold:" + String(getPowerThreshold(), 5));
Serial.println(" asnr:" + String(getAsnr(), 5));
Serial.println(" NR_alpha:" + String(getNRAlpha(), 5));
Serial.println(" NR_width:" + String(getNRWidth(), 5));
Serial.flush();
}
enable(true);
return is_enabled;
}
void AudioSpectralDenoise_F32::update(void)
{
//get a pointer to the latest data
audio_block_f32_t *in_audio_block = AudioStream_F32::receiveReadOnly_f32();
if (!in_audio_block)
return;
//simply return the audio if this class hasn't been enabled
if (!is_enabled) {
AudioStream_F32::transmit(in_audio_block);
AudioStream_F32::release(in_audio_block);
return;
}
//******************************************************************************
//convert to frequency domain
//FFT is in complex_2N_buffer, interleaved real, imaginary, real, imaginary, etc
myFFT.execute(in_audio_block, complex_2N_buffer);
// Preserve the block id, so we can pass it out with our final result
unsigned long incoming_id = in_audio_block->id;
// We just passed ownership of in_audio_block to myFFT, so we can
// release it here as we won't use it here again.
AudioStream_F32::release(in_audio_block);
if (init_phase == 1) {
if (serial_debug) {
Serial.println("One time init");
Serial.flush();
}
init_phase++;
for (int bindx = 0; bindx < N_bins; bindx++) {
NR_G[bindx] = 1.0;
NR_Hk_old[bindx] = 1.0; // old gain or xu in development mode
NR_Nest[bindx] = 0.0;
pslp[bindx] = 0.5;
}
}
//******************************************************************************
//***** Calculate magnitude, used later for noise estimates and calculations
// AIUI, as we are only passing real values into a complex FFT, the resulting
// data contains duplicated mirrored data, thus we only need to evaluate the
// magnitude of the first half of the bins, as it will be identical to that
// of the second half of the bins. When we finally apply the NR results to the
// FFT data we apply it to both the first half and the conjugate, mirror style.
// Fundamentally, this saves us half the processing on some parts.
for (int bindx = 0; bindx < N_bins; bindx++) {
NR_X[bindx] =
(complex_2N_buffer[bindx * 2] * complex_2N_buffer[bindx * 2] +
complex_2N_buffer[bindx * 2 + 1] * complex_2N_buffer[bindx * 2 + 1]);
}
//Second stage initialisation
if (init_phase == 2) {
static int NR_init_counter = 0;
if (serial_debug) {
Serial.println("Two time init (" + String(NR_init_counter) + ")");
Serial.flush();
}
for (int bindx = 0; bindx < N_bins; bindx++) {
// we do it 20 times to average over 20 frames for app. 100ms only on
// NR_on/bandswitch/modeswitch,...
NR_Nest[bindx] = NR_Nest[bindx] + 0.05 * NR_X[bindx];
xt[bindx] = psini * NR_Nest[bindx];
}
NR_init_counter++;
if (NR_init_counter > 19) //average over 20 frames for app. 100ms
{
if (serial_debug) {
Serial.println("Two time init done");
Serial.flush();
}
NR_init_counter = 0;
init_phase++;
}
if (serial_debug)
Serial.println(" Two time loop done");
}
//Now we are fully initialised, we can actually do the NR processing
//******************************************************************************
//MMSE (Minimum Mean Square Error) based noise estimate
// code/algo inspired by the matlab based voicebox library:
// http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
// Noise estimate code can be found at:
// https://github.com/YouriT/matlab-speech/blob/master/MATLAB_CODE_SOURCE/voicebox/estnoiseg.m
for (int bindx = 0; bindx < N_bins; bindx++) {
float32_t xtr;
// a-posteriori speech presence probability
ph1y[bindx] = 1.0 / (1.0 + pfac * expf(xih1r * NR_X[bindx] / xt[bindx]));
// smoothed speech presence probability
pslp[bindx] = ap * pslp[bindx] + (1.0 - ap) * ph1y[bindx];
// limit ph1y
if (pslp[bindx] > psthr) {
ph1y[bindx] = 1.0 - pnsaf;
} else {
ph1y[bindx] = fmin(ph1y[bindx], 1.0);
}
// estimated raw noise spectrum
xtr = (1.0 - ph1y[bindx]) * NR_X[bindx] + ph1y[bindx] * xt[bindx];
// smooth the noise estimate
xt[bindx] = ax * xt[bindx] + (1.0 - ax) * xtr;
}
// Limit the ratios
// I don't have a lot of info on how this works, but SNRpost and SNRprio are related
// to both Ephraim&Malah(84) and Romanin(2009) papers
for (int bindx = 0; bindx < N_bins; bindx++) {
// limited to +30 /-15 dB, might be still too much of reduction, let's try it?
NR_SNR_post[bindx] = fmax(fmin(NR_X[bindx] / xt[bindx], 1000.0), snr_prio_min);
NR_SNR_prio[bindx] =
fmax(NR_alpha * NR_Hk_old[bindx] +
(1.0 - NR_alpha) * fmax(NR_SNR_post[bindx] - 1.0, 0.0), 0.0);
}
//******************************************************************************
// VAD
// maybe we should limit this to the signal containing bins (filtering!!)
for (int bindx = VAD_low; bindx < VAD_high; bindx++) {
float32_t v =
NR_SNR_prio[bindx] * NR_SNR_post[bindx] / (1.0 + NR_SNR_prio[bindx]);
NR_G[bindx] = 1.0 / NR_SNR_post[bindx] * sqrtf((0.7212 * v + v * v));
NR_Hk_old[bindx] = NR_SNR_post[bindx] * NR_G[bindx] * NR_G[bindx];
}
//******************************************************************************
// Do the musical noise reduction
// musical noise "artefact" reduction by dynamic averaging - depending on SNR ratio
pre_power = 0.0;
post_power = 0.0;
for (int bindx = VAD_low; bindx < VAD_high; bindx++) {
pre_power += NR_X[bindx];
post_power += NR_G[bindx] * NR_G[bindx] * NR_X[bindx];
}
power_ratio = post_power / pre_power;
if (power_ratio > power_threshold) {
power_ratio = 1.0;
NN = 1;
} else {
NN = 1 + 2 * (int)(0.5 +
NR_width * (1.0 - power_ratio / power_threshold));
}
for (int bindx = VAD_low + NN / 2; bindx < VAD_high - NN / 2; bindx++) {
NR_Nest[bindx] = 0.0;
for (int m = bindx - NN / 2; m <= bindx + NN / 2; m++) {
NR_Nest[bindx] += NR_G[m];
}
NR_Nest[bindx] /= (float32_t) NN;
}
// and now the edges - only going NN steps forward and taking the average
// lower edge
for (int bindx = VAD_low; bindx < VAD_low + NN / 2; bindx++) {
NR_Nest[bindx] = 0.0;
for (int m = bindx; m < (bindx + NN); m++) {
NR_Nest[bindx] += NR_G[m];
}
NR_Nest[bindx] /= (float32_t) NN;
}
// upper edge - only going NN steps backward and taking the average
for (int bindx = VAD_high - NN; bindx < VAD_high; bindx++) {
NR_Nest[bindx] = 0.0;
for (int m = bindx; m > (bindx - NN); m--) {
NR_Nest[bindx] += NR_G[m];
}
NR_Nest[bindx] /= (float32_t) NN;
}
// end of edge treatment
for (int bindx = VAD_low + NN / 2; bindx < VAD_high - NN / 2; bindx++) {
NR_G[bindx] = NR_Nest[bindx];
}
// end of musical noise reduction
//******************************************************************************
// And finally actually apply the weightings to the signals...
// FINAL SPECTRAL WEIGHTING: Multiply current FFT results with complex_2N_buffer for
// bins with the bin-specific gain factors G
for (int bindx = 0; bindx < N_bins; bindx++) {
// real part
complex_2N_buffer[bindx * 2] = complex_2N_buffer[bindx * 2] * NR_G[bindx];
// imag part
complex_2N_buffer[bindx * 2 + 1] =
complex_2N_buffer[bindx * 2 + 1] * NR_G[bindx];
// real part conjugate symmetric
//N_bins * 4 == N_FFT * 2 == N_FFT[real, imag]
complex_2N_buffer[N_bins * 4 - bindx * 2 - 2] =
complex_2N_buffer[N_bins * 4 - bindx * 2 - 2] * NR_G[bindx];
// imag part conjugate symmetric
complex_2N_buffer[N_bins * 4 - bindx * 2 - 1] =
complex_2N_buffer[N_bins * 4 - bindx * 2 - 1] * NR_G[bindx];
}
//******************************************************************************
//And finally call the IFFT, back to the time domain, and pass the processed block on
//out_block is pre-allocated in here.
audio_block_f32_t *out_audio_block = myIFFT.execute(complex_2N_buffer);
//update the block number to match the incoming one
out_audio_block->id = incoming_id;
//send the returned audio block. Don't issue the release command here because myIFFT will re-use it
//don't release this buffer because myIFFT re-uses it within its own code
AudioStream_F32::transmit(out_audio_block); //don't release this buffer because myIFFT re-uses it within its own code
return;
}

@ -0,0 +1,198 @@
/*
* AudioSpectralDenoise_F32
*
* Created: Graham Whaley, 2022
* Purpose: Spectral noise reduction
*
* This processes a single stream of audio data (i.e., it is mono)
*
* License: GNU GPLv3 License
* As the code it is derived from is GPLv3
*
* Based off the work from the UHSDR project, as also used in the mcHF and Convolution-SDR
* projects.
* Reference documentation can be found at https://github.com/df8oe/UHSDR/wiki/Noise-reduction
* Code extracted into isolated files can be found at
* https://github.com/grahamwhaley/DSPham/blob/master/spectral.cpp
*/
#ifndef _AudioSpectralDenoise_F32_h
#define _AudioSpectralDenoise_F32_h
#include "AudioStream_F32.h"
#include <arm_math.h>
#include "FFT_Overlapped_OA_F32.h"
#include <Arduino.h>
class AudioSpectralDenoise_F32:public AudioStream_F32 {
//GUI: inputs:1, outputs:1 //this line used for automatic generation of GUI node
//GUI: shortName:spectral
public:
AudioSpectralDenoise_F32(void):AudioStream_F32(1, inputQueueArray_f32) {
};
AudioSpectralDenoise_F32(const AudioSettings_F32 &
settings):AudioStream_F32(1, inputQueueArray_f32) {
}
AudioSpectralDenoise_F32(const AudioSettings_F32 & settings,
const int _N_FFT):AudioStream_F32(1,
inputQueueArray_f32)
{
setup(settings, _N_FFT);
}
//destructor...release all of the memory that has been allocated
~AudioSpectralDenoise_F32(void) {
if (complex_2N_buffer) delete complex_2N_buffer;
if (NR_X) delete NR_X;
if (ph1y) delete ph1y;
if (pslp) delete pslp;
if (xt) delete xt;
if (NR_SNR_post) delete NR_SNR_post;
if (NR_SNR_prio) delete NR_SNR_prio;
if (NR_Hk_old) delete NR_Hk_old;
if (NR_G) delete NR_G;
if (NR_Nest) delete NR_Nest;
}
//Our default FFT size is 256. That is time and space efficient, but
// if you are running at a 'high' sample rate, the NR 'buckets' might
// be quite small. You may want to use a 1024 FFT if running at 44.1KHz
// for instance, if you can afford the time and space overheads.
int setup(const AudioSettings_F32 & settings, const int _N_FFT = 256);
virtual void update(void);
bool enable(bool state = true) {
is_enabled = state;
return is_enabled;
}
bool enabled(void) {
return is_enabled;
}
//Getters and Setters
float32_t getAsnr(void) {
return asnr;
}
void setAsnr(float32_t v) {
asnr = v;
}
float32_t getVADHighFreq(void) {
return VAD_high_freq;
}
void setVADHighFreq(float32_t f) {
VAD_high_freq = f;
}
float32_t getVADLowFreq(void) {
return VAD_low_freq;
}
void setVADLowFreq(float32_t f) {
VAD_low_freq = f;
}
float32_t getNRAlpha(void) {
return NR_alpha;
}
void setNRAlpha(float32_t v) {
NR_alpha = v;
if (NR_alpha < 0.9)
NR_alpha = 0.9;
if (NR_alpha > 0.9999)
NR_alpha = 0.9999;
}
float32_t getSNRPrioMin(void) {
return snr_prio_min;
}
void setSNRPrioMin(float32_t v) {
snr_prio_min = v;
}
int16_t getNRWidth(void) {
return NR_width;
}
void setNRWidth(int16_t v) {
NR_width = v;
}
float32_t getPowerThreshold(void) {
return power_threshold;
}
void setPowerThreshold(float32_t v) {
power_threshold = v;
}
float32_t getTaxFactor(void) {
return tax_factor;
}
void setTaxFactor(float32_t v) {
tax_factor = v;
}
float32_t getTapFactor(void) {
return tap_factor;
}
void setTapFactor(float32_t v) {
tap_factor = v;
}
private:
static const int max_fft = 2048; //The largest FFT FFT_OA handles. Fixed so we can fix the
//array sizes - FIXME - a hack, but easier than doing the dynamic allocations for now.
uint8_t init_phase = 1; //Track our phases of initialisation
int is_enabled = 0;
float32_t *complex_2N_buffer; //Store our FFT real/imag data
audio_block_f32_t *inputQueueArray_f32[1]; //memory pointer for the input to this module
FFT_Overlapped_OA_F32 myFFT;
IFFT_Overlapped_OA_F32 myIFFT;
int N_FFT = -1; //How big an FFT are we using?
int N_bins = -1; //How many actual data bins are we processing on
float sample_rate_Hz = AUDIO_SAMPLE_RATE;
//*********** NR vars
//Magnitudes (fabs) of power for the last four (three?) audio blocks
float32_t *NR_X = NULL;
float32_t *ph1y = NULL;
float32_t *pslp = NULL;
float32_t *xt = NULL;
const float32_t psini = 0.5; //initial speech probability
const float32_t pspri = 0.5; //prior speech probability
float32_t asnr = 25; //active SNR in dB - seems to make less different than I expected.
float32_t xih1;
float32_t pfac;
float32_t xih1r;
const float32_t psthr = 0.99; //threshold for smoothed speech probability
const float32_t pnsaf = 0.01; //noise probability safety value
float32_t tinc; //Frame time in seconds
float32_t tax_factor = 0.8; //Noise output smoothing factor
float32_t tax; //noise output smoothing constant in seconds = -tinc/ln(0.8)
float32_t tap_factor = 0.9; //Speech probability smoothing factor
float32_t tap; //speech prob smoothing constant in seconds = -tinc/ln(0.9)
float32_t ap; //noise output smoothing factor
float32_t ax; //noise output smoothing factor
float32_t snr_prio_min = powf(10, -(float32_t) 20 / 20.0); //Lower limit of SNR ratio calculation
// Time smoothing of gain weights. Makes quite a difference to the NR performance.
float32_t NR_alpha = 0.99; //range 0.98-0.9999. 0.95 acts much too hard: reverb effects.
float32_t *NR_SNR_post = NULL;
float32_t *NR_SNR_prio = NULL;
float32_t *NR_Hk_old = NULL;
// preliminary gain factors (before time smoothing) and after that contains the frequency
// smoothed gain factors
float32_t *NR_G = NULL;
//Our Noise estimate array - 'one dimentional' is a hangover from the old version of the
// original code that used multiple entries for averaging, which seems to have then been
// dropped, but the arrays still left in place.
float32_t *NR_Nest = NULL;
float32_t VAD_low_freq = 100.0;
float32_t VAD_high_freq = 3600.0;
//if we grow the FFT to 1024, these might need to be bigger than a uint8?
uint8_t VAD_low, VAD_high; //lower/upper bounds for 'voice spectrum' slot processing
int16_t NN; //used as part of VAD calculations, n-bin averaging?. Also, why an int16 ?
int16_t NR_width = 4;
float32_t pre_power, post_power; //Used in VAD calculations
float32_t power_ratio;
float32_t power_threshold = 0.4;
};
#endif

@ -21,6 +21,7 @@
#include "AudioMixer_F32.h"
#include "AudioMultiply_F32.h"
#include "AudioSettings_F32.h"
#include "AudioSpectralDenoise_F32.h"
#include "input_i2s_f32.h"
#include "input_spdif3_f32.h"
#include "async_input_spdif3_F32.h"

@ -386,6 +386,7 @@ span.mainfunction {color: #993300; font-weight: bolder}
{"type":"AudioLMSDenoiseNotch_F32","data":{"defaults":{"name":{"value":"new"}},"shortName":"LMS","inputs":"1","output":"0","category":"filter-function","color":"#E6E0F8","icon":"arrow-in.png","outputs":"1"}},
{"type":"AudioSpectralDenoise_F32","data":{"defaults":{"name":{"value":"new"}},"shortName":"Spectral","inputs":"1","output":"0","category":"filter-function","color":"#E6E0F8","icon":"arrow-in.png","outputs":"1"}},
{"type":"AudioFilterFreqWeighting_F32","data":{"defaults":{"name":{"value":"new"}},"shortName":"freqWeight","inputs":"NaN","output":"0","category":"filter-function","color":"#E6E0F8","icon":"arrow-in.png","outputs":"NaN"}},
{"type":"AudioFilterTimeWeighting_F32","data":{"defaults":{"name":{"value":"new"}},"shortName":"timeWeight","inputs":"1","output":"0","category":"filter-function","color":"#E6E0F8","icon":"arrow-in.png","outputs":"1"}},
{"type":"AudioMathAdd_F32","data":{"defaults":{"name":{"value":"new"}},"shortName":"mathAdd","inputs":"2","output":"0","category":"math-function","color":"#E6E0F8","icon":"arrow-in.png","outputs":"1"}},
@ -904,6 +905,114 @@ See Compressor and Compressor2 for complete, ready to use classes.</p>
</div>
</script>
<div>
<script type="text/x-red" data-help-name="AudioSpectralDenoise_F32">
<!-- ============ AudioSpectralDenoise_F32 ========= -->
<h3>Summary</h3>
<div class=tooltipinfo>
<p>Spectral Noise Reduction</p>
<p>Remove random noise, such as background noise or machine generated noise, and try
to leave voice intact.</p>
<p>Spectral NR does not try to remove constant tones or clicks and pops.</p>
<p>Spectral NR is generally not useful for cleaning up 'pure tone' sources, such as morse code.</p>
<h3>Audio Connections</h3>
<table class=doc align=center cellpadding=3>
<tr class=top><th>Port</th><th>Purpose</th></tr>
<tr class=odd><td align=center>In 0</td><td>Signal to be filtered</td></tr>
<tr class=odd><td align=center>Out 0</td><td>Filtered Signal Output</td></tr>
</table>
<h3>Functions</h3>
<p class=func><span class=keyword>setup</span>(<strong>AudioSettings_F32 &</strong>settings, <strong>int</strong> n_fft);</p>
<p class=desc>Setup the filter. Must be called if parameters not passed in to the
constructor. Can be re-called if other parameters have been changed to have
the changes take effect.
The maximum FFT size is dictated by the underlying FFT_OA code.
</p>
<p class=func><span class=keyword>enable</span>(<strong>bool</strong> start);</p>
<p class=desc>Turn on or offthe filter. The filter is enabled by default upon
creation.
</p>
<p class=func><span class=keyword>enabled</span>();</p>
<p class=desc>Return the enabled state of the filter.</p>
<p class=func><span class=keyword>getAsnr</span>();</p>
<p class=desc>Return the current ASNR value.</p>
<p class=func><span class=keyword>setAsnr</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the ASNR value. Active SNR.</p>
<p class=desc>Call <span class=keyword>setup()</span> for the change to take effect.</p>
<p class=func><span class=keyword>getVADHighFreq</span>();</p>
<p class=desc>Get the VAD band high frequency cutoff value.</p>
<p class=func><span class=keyword>setVADHighFreq</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the VAD High Frequency cutoff value. Sets the high
frequency value of the Voice Activity Detector (VAD) window.
</p>
<p class=desc>Call <span class=keyword>setup()</span> for the change to take effect.</p>
<p class=func><span class=keyword>getVADLowFreq</span>();</p>
<p class=desc>Get the VAD band low frequency value.</p>
<p class=func><span class=keyword>setVADLowFreq</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the VAD band low frequency value.</p>
<p class=desc>Call <span class=keyword>setup()</span> for the change to take effect.</p>
<p class=func><span class=keyword>getNRAlpha</span>();</p>
<p class=desc>get the NRalpha value. NR alpha is the time smoothing constant. It's
range is clipped between 0.9 and 0.9999 in the code. Settings >0.95
generally recommended, or you can get strong reverb and 'watery' effects.
</p>
<p class=func><span class=keyword>setNRAlpha</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the NRalpha value.</p>
<p class=desc>The change takes effect immediately.</p>
<p class=func><span class=keyword>getSNRPrioMin</span>();</p>
<p class=desc>Get the SNR prior value.</p>
<p class=func><span class=keyword>setSNRPrioMin</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the SNR prior value.</p>
<p class=desc>The change takes effect immediately.</p>
<p class=func><span class=keyword>getNRWidth</span>();</p>
<p class=desc>Get the NR width. Used for the measuring 'span' in the musical note
reduction code.
</p>
<p class=func><span class=keyword>setNRWidth</span>(<strong>int16_t</strong> value);</p>
<p class=desc>Set the NR width.</p>
<p class=desc>The change takes effect immediately.</p>
<p class=func><span class=keyword>getPowerThreshold</span>();</p>
<p class=desc>Get the power threshold. Used to limit the effects of the musical
note reduction code.
</p>
<p class=func><span class=keyword>setPowerThreshold</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the power threshold.</p>
<p class=desc>The change takes effect immediately.</p>
<p class=func><span class=keyword>getTaxFactor</span>();</p>
<p class=desc>Get the noise output smoothing factor.</p>
<p class=func><span class=keyword>setTaxFactor</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the noise output smoothing factor. Typical values are around 0.8.</p>
<p class=desc>Call <span class=keyword>setup()</span> for the change to take effect.</p>
<p class=func><span class=keyword>getTapFactor</span>();</p>
<p class=desc>Get the speech probability smoothing factor.</p>
<p class=func><span class=keyword>setTapFactor</span>(<strong>float32_t</strong> value);</p>
<p class=desc>Set the speech probability smoothing factor. Typical values are around 0.9.</p>
<p class=desc>Call <span class=keyword>setup()</span> for the change to take effect.</p>
<h3>Examples</h3>
<p class=exam>File &gt; Examples &gt; OpenAudio_ArduinoLibrary &gt; SpectralDenoise.ino
</p>
<h3>Notes</h3>
<p> The defaults work OK, but it is worth experimenting with the adjustable
parameters for your specific situation. Try adjusting NRAlpha, NRWidth and Asnr.
</p>
<p> The default FFT size is 256. You may wish to make this larger, which will allow
a finer granularity of 'buckets' to potentially improve the noise reduction, but,
will cost both processor time and memory overheads.
</p>
<p> The maximum FFT size is set by the underlying <strong>FFT_OA</strong> code, which at
time of writing limits the maximum to 2048.
</p>
<p> The code is written to account for the <strong>sample_rate_Hz</strong> passed in with
the audio settings, <strong>but</strong>, this has not been tested. If you do test,
either with a higher or lower sample rate, please report back and consider fixing
this documentation.
</p>
<h3>References</h3>
<p>The best reference on how the Spectral code was designed and works can be found on the
<a href="https://github.com/df8oe/UHSDR/wiki/Noise-reduction"> UHSDR wiki </a>
</p>
</script>
</div>
<div>
<script type="text/x-red" data-help-name="AudioFilter90Deg_F32">

@ -0,0 +1,124 @@
/* Spectral Noise reduction test program.
*
* The example takes sound in from both the I2S and USB of the Teensy/audio-daughtercard,
* processes it, and sends it back out the I2S/headphone ports.
* Every 10 seconds it switches from Spectral processing to data-passthrough and back,
* to aid comparison.
* Some information is printed on the serial monitor.
*
* This example requires the Teensy Board 'USB Type' in the Arduino Tools menu to be set
* to a type that includes 'Audio', and ideally 'Serial' as well. Tested with
* 'Serial+MIDI+Audio'.
* If you do not set 'Audio', you will get a compliation errors similar to:
* "... OpenAudio_ArduinoLibrary/USB_Audio_F32.h: In member function 'virtual void AudioOutputUSB_F32::update()':"
* "... OpenAudio_ArduinoLibrary/USB_Audio_F32.h:139:3: error: 'usb_out' was not declared in this scope"
*
* MIT License. use at your own risk.
*/
#include "OpenAudio_ArduinoLibrary.h"
#include "AudioStream_F32.h"
#include "USB_Audio_F32.h"
#include <Audio.h>
#include <Wire.h>
#include <SPI.h>
#include <SD.h>
#include <SerialFlash.h>
// GUItool: begin automatically generated code
AudioInputI2S_F32 audioInI2S1; //xy=117,343
AudioInputUSB_F32 audioInUSB1; //xy=146,397
AudioMixer4_F32 input_mixer; //xy=370,321
AudioSpectralDenoise_F32 Spectral; //xy=852,250
AudioMixer4_F32 output_mixer; //xy=993,296
AudioSwitch4_OA_F32 processing_switch;
AudioOutputI2S_F32 audioOutI2S1; //xy=1257,367
AudioOutputUSB_F32 audioOutUSB1; //xy=1261,418
//Inputs - mixed into one stream
AudioConnection_F32 patchCord1(audioInI2S1, 0, input_mixer, 0);
AudioConnection_F32 patchCord2(audioInUSB1, 0, input_mixer, 1);
//route through a switch, so we can switch Spectral in/out
AudioConnection_F32 patchCord3(input_mixer, 0, processing_switch, 0);
//First route is direct - direct to the output mixer
AudioConnection_F32 patchCord4(processing_switch, 0, output_mixer, 0);
//Second route is through Spectral to the output mixer
AudioConnection_F32 patchCord5(processing_switch, 1, Spectral, 0);
AudioConnection_F32 patchCord6(Spectral, 0, output_mixer, 1);
//And finally output the mixer to the output channels
AudioConnection_F32 patchCord7(output_mixer, 0, audioOutI2S1, 0);
AudioConnection_F32 patchCord8(output_mixer, 0, audioOutI2S1, 1);
AudioConnection_F32 patchCord9(output_mixer, 0, audioOutUSB1, 0);
AudioConnection_F32 patchCord10(output_mixer, 0, audioOutUSB1, 1);
AudioControlSGTL5000 sgtl5000_1; //xy=519,146
// GUItool: end automatically generated code
AudioSettings_F32 audio_settings(AUDIO_SAMPLE_RATE_EXACT, AUDIO_BLOCK_SAMPLES);
int current_cycle = 0; //Choose how we route the audio - to process or not
static void spectralSetup(void){
//Use a 1024 FFT in this example
if (Spectral.setup(audio_settings, 1024) < 0 ) {
Serial.println("Failed to setup Spectral");
} else {
Serial.println("Spectral setup OK");
}
Serial.flush();
}
//The setup function is called once when the system starts up
void setup(void) {
//Start the USB serial link (to aid debugging)
Serial.begin(115200); delay(500);
Serial.println("Setup starting...");
//Allocate dynamically shuffled memory for the audio subsystem
AudioMemory(30); AudioMemory_F32(30);
Serial.println("Calling Spectral setup");
spectralSetup();
Serial.println("Spectral Setup done");
sgtl5000_1.enable();
sgtl5000_1.unmuteHeadphone();
sgtl5000_1.volume(0.5);
//End of setup
Serial.println("Setup complete.");
};
//After setup(), the loop function loops forever.
//Note that the audio modules are called in the background.
//They do not need to be serviced by the loop() function.
void loop(void) {
// every 'n' seconds move to the next cycle of processing.
if ( ((millis()/1000) % 10) == 0 ) {
current_cycle++;
if (current_cycle >= 2) current_cycle = 0;
switch( current_cycle ) {
case 0:
Serial.println("Passthrough");
processing_switch.setChannel(0);
break;
case 1:
Serial.println("Run Spectral NR");
processing_switch.setChannel(1);
break;
default:
current_cycle = 0; //oops - reset to start
break;
}
}
//Nap - we don't need to hard-spin...
delay(1000);
};
Loading…
Cancel
Save