public static string DecodeKey(string resp)
{
var context = new Context();
context.Eval(JS);
var concatFunction = context.GetVariable("decodeKey").As <Function>();
string key = concatFunction.Call(new Arguments {
resp
}).ToString();
byte[] v = Array.ConvertAll(key.Trim(',').Split(','), s => (byte)int.Parse(s));
string realKey = Convert.ToBase64String(v);
return(realKey);
}
public void getFullSpectrumThreaded()
{
try
{
// We only need to retain the samples for combined channels over the time domain
float[] preProcessedSamples = new float[this.numTotalSamples];
int numProcessed = 0;
float combinedChannelAverage = 0f;
for (int i = 0; i < multiChannelSamples.Length; i++)
{
combinedChannelAverage += multiChannelSamples[i];
// Each time we have processed all channels samples for a point in time, we will store the average of the channels combined
if ((i + 1) % this.numChannels == 0)
{
preProcessedSamples[numProcessed] = combinedChannelAverage / this.numChannels;
numProcessed++;
combinedChannelAverage = 0f;
}
}
Debug.Log("Combine Channels done");
Debug.Log(preProcessedSamples.Length);
// Once we have our audio sample data prepared, we can execute an FFT to return the spectrum data over the time domain
int spectrumSampleSize = 1024;
int iterations = preProcessedSamples.Length / spectrumSampleSize;
FFT fft = new FFT();
fft.Initialize((System.UInt32)spectrumSampleSize);
Debug.Log(string.Format("Processing {0} time domain samples for FFT", iterations));
double[] sampleChunk = new double[spectrumSampleSize];
for (int i = 0; i < iterations; i++)
{
// Grab the current 1024 chunk of audio sample data
Array.Copy(preProcessedSamples, i * spectrumSampleSize, sampleChunk, 0, spectrumSampleSize);
// Apply our chosen FFT Window
double[] windowCoefs = DSP.Window.Coefficients(DSP.Window.Type.Hanning, (uint)spectrumSampleSize);
double[] scaledSpectrumChunk = DSP.Math.Multiply(sampleChunk, windowCoefs);
double scaleFactor = DSP.Window.ScaleFactor.Signal(windowCoefs);
// Perform the FFT and convert output (complex numbers) to Magnitude
Complex[] fftSpectrum = fft.Execute(scaledSpectrumChunk);
double[] scaledFFTSpectrum = DSPLib.DSP.ConvertComplex.ToMagnitude(fftSpectrum);
scaledFFTSpectrum = DSP.Math.Multiply(scaledFFTSpectrum, scaleFactor);
// These 1024 magnitude values correspond (roughly) to a single point in the audio timeline
float curSongTime = getTimeFromIndex(i) * spectrumSampleSize;
// Send our magnitude data off to our Spectral Flux Analyzer to be analyzed for peaks
audPP.analyzeSpectrum(Array.ConvertAll(scaledFFTSpectrum, x => (float)x), curSongTime);
}
Debug.Log("Spectrum Analysis done");
Debug.Log("Background Thread Completed");
Debug.Log("Number of Points: " + audPP.spectralFluxSamples.Count);
for (int i = 0; i < audPP.spectralFluxSamples.Count; i++)
{
if (audPP.spectralFluxSamples[i].time < 2)
{
continue;
}
if (i % 20 == 0)
{
times.Add(audPP.spectralFluxSamples[i].time);
}
}
startSong = true;
}
catch (System.Exception e)
{
// Catch exceptions here since the background thread won't always surface the exception to the main thread
Debug.Log(e.ToString());
}
}
public static byte[] ToByteArray(this string value, char separator)
{
return(Array.ConvertAll(value.Split(separator), s => byte.Parse(s)));
}
