public override void Draw(RenderWindow window)
{
if (!isFirstFrame)
{
waitForEnter();
}
else
{
isFirstFrame = false;
}
double[] samples = new double[BufferSize];
for (int i = 0; i < rendersTillStop; i++)
{
if (offset + BufferSize < SampleCount)
{
for (int counter = 0; counter < BufferSize; counter++) //one line of spectogram
{
samples[counter] = Samples[offset + counter]; //4k samples
}
offset += (int)BufferSize;
//filter out high frequencies and downsample audio data
var cutOffData = AudioProcessor.DownSample(samples, downSampleCoef, SampleRate); //1k samples
for (int index = 0; index < BufferSize / downSampleCoef; index++)
{
//data[index * 2] = cutOffData[index] * hammingWindow[index]; //apply hamming window
bins[index * 2] = cutOffData[index] * this.window[index]; //apply hamming window
bins[index * 2 + 1] = 0d; //set 0s for Img complex values
}
FastFourierTransformation.FFT(bins); //apply fft
//1024 = WIDTH 700=HEIGHT
Render(bins, new Vector2f(100 + i * (1024 / rendersTillStop), 700), (int)BufferSize / (2 * downSampleCoef));
window.Draw(VA);
}
else
{
//Draw last frame and then close window
window.Draw(instructionsText);
window.Display();
waitForEnter();
window.Close();
return;
}
}
window.Draw(instructionsText);
}
public override void Update()
{
int offset = (int)(Song.PlayingOffset.AsSeconds() * SampleRate);
timeText.DisplayedString = Song.PlayingOffset.AsSeconds().ToString();
double[] samples = new double[BufferSize]; //allocate array taht will be used at downsampling
Task t1 = Task.Factory.StartNew(() => SetImaginary());
if (offset + BufferSize < SampleCount)
{
if (ChannelCount == 2)
{
for (uint i = 0; i < BufferSize; i++)
{
samples[i] = Samples[(i + offset) * 2];
}
}
else
{
for (uint i = 0; i < BufferSize; i++)
{
samples[i] = Samples[(i + offset)];
}
}
}
//Filter out frequencies and then downsamples
var cutOffData = AudioProcessor.DownSample(samples, downSampleCoef, SampleRate);
t1.Wait(); //t1 is working with bin array
//enter complex values to the bin array
for (int i = 0; i < BufferSize / downSampleCoef; i++)
{
bin[i * 2] = cutOffData[i] * window[i];
}
FastFourierTransformation.FFT(bin);
for (uint i = 0; i < BufferSize / (2 * downSampleCoef); i++)
{
VA[i] = new Vertex(new Vector2f((float)(i * 0.5 * downSampleCoef + 100),
(float)(200 - AudioRecognitionLibrary.Tools.Arithmetics.GetComplexAbs(bin[2 * i], bin[2 * i + 1]) / 100000))); //100000 is to scale visualisation so it fits the window
}
}
/// <summary>
/// Creates fingerprint of given audio and returns it together with number of valid notes. (needed to compute recognition accuracy). <br></br>
/// </summary>
/// <param name="audio">Audio whos fingerprint we want.</param>
/// <returns>Tuple where Item1 is fingerprint, Item2 is total number of notes. <br></br>fingerprint: [hash; (absolute anchor times)]<br></br>Note: Address is the hash.</returns>
public Tuple <Dictionary <uint, List <uint> >, int> GetAudioFingerprint(IAudioFormat audio)
{
short[] monoData = AudioProcessor.ConvertToMono(audio);
double[] data = Array.ConvertAll(monoData, item => (double)item);
//compute downsample coeficient for resampling to Parameters.TargetSamplingRate
int downsampleCoef = (int)audio.SampleRate / Parameters.TargetSamplingRate;
double[] downsampledData = AudioProcessor.DownSample(data, downsampleCoef, audio.SampleRate);
int bufferSize = Parameters.WindowSize / downsampleCoef; //default: 4096/4 = 1024
var timeFrequencyPoints = CreateTimeFrequencyPoints(bufferSize, downsampledData);
//[hash;(AbsAnchorTimes)]
Dictionary <uint, List <uint> > fingerprint = CreateRecordAddresses(timeFrequencyPoints);
return(new Tuple <Dictionary <uint, List <uint> >, int> (fingerprint, timeFrequencyPoints.Count));
}
