using NUnit.Framework;

using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

namespace Spectrogram.Tests

{

class TestAGC

{

[Test]

public void Test_AGC_off()

{

string wavFilePath = "../../../../../data/qrss-10min.wav";

(double[] audio, int sampleRate) = AudioFile.ReadWAV(wavFilePath);

int fftSize = 8192;

var spec = new SpectrogramGenerator(sampleRate, fftSize, stepSize: 2000, maxFreq: 3000);

spec.Add(audio);

spec.SaveImage("qrss-agc-off.png", intensity: 3);

}

[Test]

public void Test_AGC_normToNoiseFloor()

{

// strategy here is to normalize to the magnitude of the quietest 20% of frequencies

string wavFilePath = "../../../../../data/qrss-10min.wav";

(double[] audio, int sampleRate) = AudioFile.ReadWAV(wavFilePath);

int fftSize = 8192;

var spec = new SpectrogramGenerator(sampleRate, fftSize, stepSize: 2000, maxFreq: 3000);

spec.Add(audio);

var ffts = spec.GetFFTs();

double normalIntensity = 2;

for (int i = 0; i < ffts.Count; i++)

{

double[] sorted = new double[ffts[i].Length];

ffts[i].CopyTo(sorted, 0);

Array.Sort(sorted);

double percentile = 0.25;

int percentileIndex = (int)(percentile * ffts[0].Length);

double floorValue = sorted[percentileIndex];

for (int y = 0; y < ffts[i].Length; y++)

{

ffts[i][y] = ffts[i][y] / floorValue * normalIntensity;

}

Console.WriteLine(floorValue);

}

spec.SaveImage("qrss-agc-norm-floor.png", intensity: 3);

}

[Test]

public void Test_AGC_normWindow()

{

// strategy here is to create a weighted moving window mean and normalize to that

string wavFilePath = "../../../../../data/qrss-10min.wav";

(double[] audio, int sampleRate) = AudioFile.ReadWAV(wavFilePath);

int fftSize = 8192;

var spec = new SpectrogramGenerator(sampleRate, fftSize, stepSize: 2000, maxFreq: 3000);

spec.Add(audio);

var ffts = spec.GetFFTs();

for (int i = 0; i < ffts.Count; i++)

ffts[i] = SubtractMovingWindowFloor(ffts[i]);

spec.SaveImage("qrss-agc-norm-window.png", intensity: 3);

}

private double[] SubtractMovingWindow(double[] input, int windowSizePx = 100)

{

// return a copy of the input array with the moving window subtracted

var hanningWindow = new FftSharp.Windows.Hanning();

double[] window = hanningWindow.Create(windowSizePx);

double windowSum = window.Sum();

double[] windowed = new double[input.Length];

double[] normalized = new double[input.Length];

for (int i = 0; i < input.Length - window.Length; i++)

{

double windowedInputSum = 0;

for (int j = 0; j < window.Length; j++)

windowedInputSum += input[i + j] * window[j];

windowed[i + window.Length / 2] = windowedInputSum / windowSum;

}

for (int i = 0; i < input.Length; i++)

normalized[i] = Math.Max(input[i] - windowed[i], 0);

return normalized;

}

private double[] SubtractMovingWindowFloor(double[] input, int windowSizePx = 20, double percentile = .2)

{

// return a copy of the input with the noise floor subtracted

// where the noise floor is calculated from a moving window

// this is good but very slow

double[] normalized = new double[input.Length];

int floorIndex = (int)(percentile * windowSizePx);

double[] segment = new double[windowSizePx];

for (int i = 0; i < input.Length - windowSizePx; i++)

{

for (int j = 0; j < windowSizePx; j++)

segment[j] = input[i + j];

Array.Sort(segment);

normalized[i] = Math.Max(input[i] - segment[floorIndex], 0);

}

return normalized;

}

}

}