public void SelectDragSpell(int s)
{
clean();
switch (s)
{
case 0:
spellText = GameObject.Instantiate(Resources.Load("SpellTexts/SpectralSwordText")) as GameObject;
Destroy(escolhida [1].icon);
escolhida[1] = new Spectral();
spellbook.UpdateSpellIcons();
break;
case 1:
spellText = GameObject.Instantiate(Resources.Load("SpellTexts/VineWallText")) as GameObject;
if (liberada [1] == 1)
{
Destroy(escolhida [1].icon);
escolhida [1] = new VineWall();
spellbook.UpdateSpellIcons();
}
else
{
buttonLiberar = GameObject.Instantiate(Resources.Load("ButC")) as GameObject;
buttonLiberar.GetComponent <CompItem>().setSpSelec(1);
}
break;
}
}
void Start()
{
usingAndSpells = false; //Bool usada para checar se o jogador está usando magia no Android
escolhida = new Spells[4]; //cria novas magias
//aloca a magia base em cada slot de magia:
escolhida [0] = new Fireball();
escolhida [1] = new Spectral();
escolhida [2] = new Explosion();
escolhida[3] = new Meteor();
spellbook.UpdateSpellIcons(); //Atualiza os icones das magias utilizadas
//sp2 e sp3 usados para checar se está utilizando as magias 3 e 4. spellCooldown
sp2 = false;
sp3 = false;
spellCooldown = 0.0f;
liberada = new int[10];
plScript = Pl.GetComponent <Player> ();
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="options">Options</param>
public SpectralFeaturesExtractor(MultiFeatureOptions options) : base(options)
{
var featureList = options.FeatureList;
if (featureList == "all" || featureList == "full")
{
featureList = FeatureSet;
}
var features = featureList.Split(',', '+', '-', ';', ':')
.Select(f => f.Trim().ToLower())
.ToList();
_parameters = options.Parameters;
_extractors = features.Select <string, Func <float[], float[], float> >(feature =>
{
switch (feature)
{
case "sc":
case "centroid":
return(Spectral.Centroid);
case "ss":
case "spread":
return(Spectral.Spread);
case "sfm":
case "flatness":
if (_parameters?.ContainsKey("minLevel") ?? false)
{
var minLevel = (float)_parameters["minLevel"];
return((spectrum, freqs) => Spectral.Flatness(spectrum, minLevel));
}
else
{
return((spectrum, freqs) => Spectral.Flatness(spectrum));
}
case "sn":
case "noiseness":
if (_parameters?.ContainsKey("noiseFrequency") ?? false)
{
var noiseFrequency = (float)_parameters["noiseFrequency"];
return((spectrum, freqs) => Spectral.Noiseness(spectrum, freqs, noiseFrequency));
}
else
{
return((spectrum, freqs) => Spectral.Noiseness(spectrum, freqs));
}
case "rolloff":
if (_parameters?.ContainsKey("rolloffPercent") ?? false)
{
var rolloffPercent = (float)_parameters["rolloffPercent"];
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs, rolloffPercent));
}
else
{
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs));
}
case "crest":
return((spectrum, freqs) => Spectral.Crest(spectrum));
case "entropy":
case "ent":
return((spectrum, freqs) => Spectral.Entropy(spectrum));
case "sd":
case "decrease":
return((spectrum, freqs) => Spectral.Decrease(spectrum));
case "c1":
case "c2":
case "c3":
case "c4":
case "c5":
case "c6":
return((spectrum, freqs) => Spectral.Contrast(spectrum, freqs, int.Parse(feature.Substring(1))));
default:
return((spectrum, freqs) => 0);
}
}).ToList();
FeatureCount = features.Count;
FeatureDescriptions = features;
_blockSize = options.FftSize > FrameSize ? options.FftSize : MathUtils.NextPowerOfTwo(FrameSize);
_fft = new RealFft(_blockSize);
var frequencies = options.Frequencies;
var resolution = (float)SamplingRate / _blockSize;
if (frequencies == null)
{
_frequencies = Enumerable.Range(0, _blockSize / 2 + 1)
.Select(f => f * resolution)
.ToArray();
}
else if (frequencies.Length == _blockSize / 2 + 1)
{
_frequencies = frequencies;
}
else
{
_frequencies = new float[frequencies.Length + 1];
frequencies.FastCopyTo(_frequencies, frequencies.Length, 0, 1);
_mappedSpectrum = new float[_frequencies.Length];
_frequencyPositions = new int[_frequencies.Length];
for (var i = 1; i < _frequencies.Length; i++)
{
_frequencyPositions[i] = (int)(_frequencies[i] / resolution) + 1;
}
}
_spectrum = new float[_blockSize / 2 + 1]; // buffer for magnitude spectrum
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="samplingRate"></param>
/// <param name="featureList"></param>
/// <param name="frameDuration"></param>
/// <param name="hopDuration"></param>
/// <param name="fftSize"></param>
/// <param name="parameters"></param>
public SpectralFeaturesExtractor(int samplingRate,
string featureList,
double frameDuration = 0.0256 /*sec*/,
double hopDuration = 0.010 /*sec*/,
int fftSize = 0,
float[] frequencies = null,
double preEmphasis = 0,
WindowTypes window = WindowTypes.Hamming,
IReadOnlyDictionary <string, object> parameters = null)
: base(samplingRate, frameDuration, hopDuration, preEmphasis)
{
if (featureList == "all" || featureList == "full")
{
featureList = FeatureSet;
}
var features = featureList.Split(',', '+', '-', ';', ':')
.Select(f => f.Trim().ToLower());
_extractors = features.Select <string, Func <float[], float[], float> >(feature =>
{
switch (feature)
{
case "sc":
case "centroid":
return(Spectral.Centroid);
case "ss":
case "spread":
return(Spectral.Spread);
case "sfm":
case "flatness":
if (parameters?.ContainsKey("minLevel") ?? false)
{
var minLevel = (float)parameters["minLevel"];
return((spectrum, freqs) => Spectral.Flatness(spectrum, minLevel));
}
else
{
return((spectrum, freqs) => Spectral.Flatness(spectrum));
}
case "sn":
case "noiseness":
if (parameters?.ContainsKey("noiseFrequency") ?? false)
{
var noiseFrequency = (float)parameters["noiseFrequency"];
return((spectrum, freqs) => Spectral.Noiseness(spectrum, freqs, noiseFrequency));
}
else
{
return((spectrum, freqs) => Spectral.Noiseness(spectrum, freqs));
}
case "rolloff":
if (parameters?.ContainsKey("rolloffPercent") ?? false)
{
var rolloffPercent = (float)parameters["rolloffPercent"];
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs, rolloffPercent));
}
else
{
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs));
}
case "crest":
return((spectrum, freqs) => Spectral.Crest(spectrum));
case "entropy":
case "ent":
return((spectrum, freqs) => Spectral.Entropy(spectrum));
case "sd":
case "decrease":
return((spectrum, freqs) => Spectral.Decrease(spectrum));
case "c1":
case "c2":
case "c3":
case "c4":
case "c5":
case "c6":
return((spectrum, freqs) => Spectral.Contrast(spectrum, freqs, int.Parse(feature.Substring(1))));
default:
return((spectrum, freqs) => 0);
}
}).ToList();
FeatureDescriptions = features.ToList();
_blockSize = fftSize > FrameSize ? fftSize : MathUtils.NextPowerOfTwo(FrameSize);
_fft = new RealFft(_blockSize);
_window = window;
_windowSamples = Window.OfType(_window, FrameSize);
var resolution = (float)samplingRate / _blockSize;
if (frequencies == null)
{
_frequencies = Enumerable.Range(0, _blockSize / 2 + 1)
.Select(f => f * resolution)
.ToArray();
}
else if (frequencies.Length == _blockSize / 2 + 1)
{
_frequencies = frequencies;
}
else
{
_frequencies = new float[frequencies.Length + 1];
frequencies.FastCopyTo(_frequencies, frequencies.Length, 0, 1);
_mappedSpectrum = new float[_frequencies.Length];
_frequencyPositions = new int[_frequencies.Length];
for (var i = 1; i < _frequencies.Length; i++)
{
_frequencyPositions[i] = (int)(_frequencies[i] / resolution) + 1;
}
}
_parameters = parameters;
_spectrum = new float[_blockSize / 2 + 1]; // buffer for magnitude spectrum
}
public Reflectivity(Spectral spectral)
{
AmbientType = ReflectivityType.Spectral;
Spectral = spectral;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="samplingRate"></param>
/// <param name="featureList"></param>
/// <param name="frameDuration"></param>
/// <param name="hopDuration"></param>
/// <param name="fftSize"></param>
/// <param name="frequencyBands"></param>
/// <param name="parameters"></param>
public Mpeg7SpectralFeaturesExtractor(int samplingRate,
string featureList,
double frameDuration = 0.0256 /*sec*/,
double hopDuration = 0.010 /*sec*/,
int fftSize = 0,
Tuple <double, double, double>[] frequencyBands = null,
WindowTypes window = WindowTypes.Hamming,
IReadOnlyDictionary <string, object> parameters = null)
: base(samplingRate, frameDuration, hopDuration)
{
if (featureList == "all" || featureList == "full")
{
featureList = FeatureSet;
}
var features = featureList.Split(',', '+', '-', ';', ':')
.Select(f => f.Trim().ToLower());
_extractors = features.Select <string, Func <float[], float[], float> >(feature =>
{
switch (feature)
{
case "sc":
case "centroid":
return(Spectral.Centroid);
case "ss":
case "spread":
return(Spectral.Spread);
case "sfm":
case "flatness":
if (parameters?.ContainsKey("minLevel") ?? false)
{
var minLevel = (float)parameters["minLevel"];
return((spectrum, freqs) => Spectral.Flatness(spectrum, minLevel));
}
else
{
return((spectrum, freqs) => Spectral.Flatness(spectrum));
}
case "sn":
case "noiseness":
if (parameters?.ContainsKey("noiseFrequency") ?? false)
{
var noiseFrequency = (float)parameters["noiseFrequency"];
return((spectrum, freqs) => Spectral.Noiseness(spectrum, freqs, noiseFrequency));
}
else
{
return((spectrum, freqs) => Spectral.Noiseness(spectrum, freqs));
}
case "rolloff":
if (parameters?.ContainsKey("rolloffPercent") ?? false)
{
var rolloffPercent = (float)parameters["rolloffPercent"];
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs, rolloffPercent));
}
else
{
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs));
}
case "crest":
return((spectrum, freqs) => Spectral.Crest(spectrum));
case "entropy":
case "ent":
return((spectrum, freqs) => Spectral.Entropy(spectrum));
case "sd":
case "decrease":
return((spectrum, freqs) => Spectral.Decrease(spectrum));
case "loud":
case "loudness":
return((spectrum, freqs) => Perceptual.Loudness(spectrum));
case "sharp":
case "sharpness":
return((spectrum, freqs) => Perceptual.Sharpness(spectrum));
default:
return((spectrum, freqs) => 0);
}
}).ToList();
FeatureDescriptions = features.ToList();
_fftSize = fftSize > FrameSize ? fftSize : MathUtils.NextPowerOfTwo(FrameSize);
_fft = new Fft(_fftSize);
_window = window;
if (_window != WindowTypes.Rectangular)
{
_windowSamples = Window.OfType(_window, FrameSize);
}
_frequencyBands = frequencyBands ?? FilterBanks.OctaveBands(6, _fftSize, samplingRate);
_filterbank = FilterBanks.Rectangular(_fftSize, samplingRate, _frequencyBands);
var cfs = _frequencyBands.Select(b => b.Item2).ToList();
// insert zero frequency so that it'll be ignored during calculations
// just like in case of FFT spectrum (0th DC component)
cfs.Insert(0, 0);
_frequencies = cfs.ToFloats();
_parameters = parameters;
// reserve memory for reusable blocks
_spectrum = new float[_fftSize / 2 + 1]; // buffer for magnitude spectrum
_mappedSpectrum = new float[_filterbank.Length + 1]; // buffer for total energies in bands
_block = new float[_fftSize]; // buffer for currently processed block
_zeroblock = new float[_fftSize]; // just a buffer of zeros for quick memset
}
public void extractFeatures()
{
//NWaves
//Initial setup
if (_filePath != null)
{
DiscreteSignal signal;
// load
var mfcc_no = 24;
var samplingRate = 44100;
var mfccOptions = new MfccOptions
{
SamplingRate = samplingRate,
FeatureCount = mfcc_no,
FrameDuration = 0.025 /*sec*/,
HopDuration = 0.010 /*sec*/,
PreEmphasis = 0.97,
Window = WindowTypes.Hamming
};
var opts = new MultiFeatureOptions
{
SamplingRate = samplingRate,
FrameDuration = 0.025,
HopDuration = 0.010
};
var tdExtractor = new TimeDomainFeaturesExtractor(opts);
var mfccExtractor = new MfccExtractor(mfccOptions);
// Read from file.
featureString = String.Empty;
featureString = $"green,";
//MFCC
var avg_vec_mfcc = new List <float>(mfcc_no + 1);
//TD Features
var avg_vec_td = new List <float>(4);
//Spectral features
var avg_vec_spect = new List <float>(10);
for (var i = 0; i < mfcc_no; i++)
{
avg_vec_mfcc.Add(0f);
}
for (var i = 0; i < 4; i++)
{
avg_vec_td.Add(0f);
}
for (var i = 0; i < 10; i++)
{
avg_vec_spect.Add(0f);
}
string specFeatures = String.Empty;
Console.WriteLine($"{tag} Reading from file");
using (var stream = new FileStream(_filePath, FileMode.Open))
{
var waveFile = new WaveFile(stream);
signal = waveFile[channel : Channels.Left];
////Compute MFCC
float[] mfvfuck = new float[25];
var sig_sam = signal.Samples;
mfccVectors = mfccExtractor.ComputeFrom(sig_sam);
var fftSize = 1024;
tdVectors = tdExtractor.ComputeFrom(signal.Samples);
var fft = new Fft(fftSize);
var resolution = (float)samplingRate / fftSize;
var frequencies = Enumerable.Range(0, fftSize / 2 + 1)
.Select(f => f * resolution)
.ToArray();
var spectrum = new Fft(fftSize).MagnitudeSpectrum(signal).Samples;
var centroid = Spectral.Centroid(spectrum, frequencies);
var spread = Spectral.Spread(spectrum, frequencies);
var flatness = Spectral.Flatness(spectrum, 0);
var noiseness = Spectral.Noiseness(spectrum, frequencies, 3000);
var rolloff = Spectral.Rolloff(spectrum, frequencies, 0.85f);
var crest = Spectral.Crest(spectrum);
var decrease = Spectral.Decrease(spectrum);
var entropy = Spectral.Entropy(spectrum);
specFeatures = $"{centroid},{spread},{flatness},{noiseness},{rolloff},{crest},{decrease},{entropy}";
//}
Console.WriteLine($"{tag} All features ready");
for (int calibC = 0; calibC < mfccVectors.Count; calibC += (mfccVectors.Count / duration) - 1)
{
featureString = String.Empty;
var tmp = new ModelInput();
for (var i = 0; i < mfcc_no; i++)
{
avg_vec_mfcc[i] = mfccVectors[calibC][i];
}
for (var i = 0; i < 4; i++)
{
avg_vec_td[i] = tdVectors[calibC][i];
}
featureString += String.Join(",", avg_vec_mfcc);
featureString += ",";
featureString += String.Join(",", avg_vec_td);
featureString += ",";
featureString += specFeatures;
Console.WriteLine($"{tag} Feature String ready {featureString}");
if (File.Exists(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp")))
{
File.Delete(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"));
File.WriteAllText(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"), featureString);
}
else
{
File.WriteAllText(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"), featureString);
}
MLContext mLContext = new MLContext();
string fileName = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp");
IDataView dataView = mLContext.Data.LoadFromTextFile <ModelInput>(
path: fileName,
hasHeader: false,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
// Use first line of dataset as model input
// You can replace this with new test data (hardcoded or from end-user application)
ModelInput sampleForPrediction = mLContext.Data.CreateEnumerable <ModelInput>(dataView, false)
.First();
ModelOutput opm = ConsumeModel.Predict(sampleForPrediction);
featureTimeList.Add(opm.Score);
Console.WriteLine($"{tag} Feature vs time list ready");
}
//Console.WriteLine($"{tag} MFCC: {mfccVectors.Count}");
//Console.WriteLine($"{tag} TD: {tdVectors.Count}");
//Console.WriteLine($"{tag} featureTimeArray: {featureTimeList.Count} {featureString}");
}
}
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="featureList"></param>
/// <param name="parameters"></param>
/// <param name="frameSize"></param>
/// <param name="hopSize"></param>
/// <param name="fftSize"></param>
public SpectralFeaturesExtractor(string featureList,
double frameSize = 0.0256 /*sec*/, double hopSize = 0.010 /*sec*/, int fftSize = 0,
IReadOnlyDictionary <string, object> parameters = null)
: base(frameSize, hopSize)
{
if (featureList == "all" || featureList == "full")
{
featureList = FeatureSet;
}
var features = featureList.Split(',', '+', '-', ';', ':');
_extractors = features.Select <string, Func <float[], float[], float> >(f =>
{
var parameter = f.Trim().ToLower();
switch (parameter)
{
case "sc":
case "centroid":
return(Spectral.Centroid);
case "ss":
case "spread":
return(Spectral.Spread);
case "sfm":
case "flatness":
if (parameters?.ContainsKey("minLevel") ?? false)
{
var minLevel = (float)parameters["minLevel"];
return((spectrum, freqs) => Spectral.Flatness(spectrum, freqs, minLevel));
}
else
{
return((spectrum, freqs) => Spectral.Flatness(spectrum, freqs));
}
case "rolloff":
if (parameters?.ContainsKey("rolloffPercent") ?? false)
{
var rolloffPercent = (float)parameters["rolloffPercent"];
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs, rolloffPercent));
}
else
{
return((spectrum, freqs) => Spectral.Rolloff(spectrum, freqs));
}
case "crest":
return((spectrum, freqs) => Spectral.Crest(spectrum));
case "sbw":
case "bandwidth":
return((spectrum, freqs) => Spectral.Bandwidth(spectrum, freqs));
case "c1":
case "c2":
case "c3":
case "c4":
case "c5":
case "c6":
return((spectrum, freqs) => Spectral.Contrast(spectrum, freqs, int.Parse(parameter.Substring(1))));
default:
throw new ArgumentException($"Unknown parameter: {parameter}");
}
}).ToArray();
FeatureDescriptions = features;
_fftSize = fftSize;
}
async public void extractFeatures(string _filepath, StorageFile sf)
{
op = new float[10];
tdVectors = new List <float[]>();
mfccVectors = new List <float[]>();
featureTimeList = new List <float[]>();
//NWaves
FilePath = _filepath;
PredictedLabel = "Ready!.";
//player.Load(GetStreamFromFile(FilePath));
//player.Play();
mMedia.Source = MediaSource.CreateFromStorageFile(sf);
bool test = player.IsPlaying;
mMedia.AutoPlay = true;
MusicProperties properties = await sf.Properties.GetMusicPropertiesAsync();
TimeSpan myTrackDuration = properties.Duration;
duration = Convert.ToInt32(myTrackDuration.TotalSeconds);
if (FilePath != null)
{
DiscreteSignal signal;
// load
var mfcc_no = 24;
var samplingRate = 44100;
var mfccOptions = new MfccOptions
{
SamplingRate = samplingRate,
FeatureCount = mfcc_no,
FrameDuration = 0.025 /*sec*/,
HopDuration = 0.010 /*sec*/,
PreEmphasis = 0.97,
Window = WindowTypes.Hamming
};
var opts = new MultiFeatureOptions
{
SamplingRate = samplingRate,
FrameDuration = 0.025,
HopDuration = 0.010
};
var tdExtractor = new TimeDomainFeaturesExtractor(opts);
var mfccExtractor = new MfccExtractor(mfccOptions);
// Read from file.
featureString = String.Empty;
featureString = $"green,";
//MFCC
var mfccList = new List <List <double> >();
var tdList = new List <List <double> >();
//MFCC
//TD Features
//Spectral features
for (var i = 0; i < mfcc_no; i++)
{
mfccList.Add(new List <double>());
}
for (var i = 0; i < 4; i++)
{
tdList.Add(new List <double>());
}
string specFeatures = String.Empty;
Console.WriteLine($"{tag} Reading from file");
using (var stream = new FileStream(FilePath, FileMode.Open))
{
var waveFile = new WaveFile(stream);
signal = waveFile[channel : Channels.Left];
////Compute MFCC
float[] mfvfuck = new float[25];
var sig_sam = signal.Samples;
mfccVectors = mfccExtractor.ComputeFrom(sig_sam);
var fftSize = 1024;
tdVectors = tdExtractor.ComputeFrom(signal.Samples);
var fft = new Fft(fftSize);
var resolution = (float)samplingRate / fftSize;
var frequencies = Enumerable.Range(0, fftSize / 2 + 1)
.Select(f => f * resolution)
.ToArray();
var spectrum = new Fft(fftSize).MagnitudeSpectrum(signal).Samples;
var centroid = Spectral.Centroid(spectrum, frequencies);
var spread = Spectral.Spread(spectrum, frequencies);
var flatness = Spectral.Flatness(spectrum, 0);
var noiseness = Spectral.Noiseness(spectrum, frequencies, 3000);
var rolloff = Spectral.Rolloff(spectrum, frequencies, 0.85f);
var crest = Spectral.Crest(spectrum);
var decrease = Spectral.Decrease(spectrum);
var entropy = Spectral.Entropy(spectrum);
specFeatures = $"{centroid},{spread},{flatness},{noiseness},{rolloff},{crest},{decrease},{entropy}";
//}
Console.WriteLine($"{tag} All features ready");
for (int calibC = 0; calibC < mfccVectors.Count;)
{
featureString = String.Empty;
var tmp = new ModelInput();
for (var j = 0; j < (mfccVectors.Count / duration) - 1 && calibC < mfccVectors.Count; j++)
{
for (var i = 0; i < mfcc_no; i++)
{
mfccList[i].Add(mfccVectors[calibC][i]);
}
for (var i = 0; i < 4; i++)
{
tdList[i].Add(tdVectors[calibC][i]);
}
calibC += 1;
}
var mfcc_statistics = new List <double>();
for (var i = 0; i < mfcc_no; i++)
{
//preheader += m + "_mean";
//preheader += m + "_min";
//preheader += m + "_var";
//preheader += m + "_sd";
//preheader += m + "_med";
//preheader += m + "_lq";
//preheader += m + "_uq";
//preheader += m + "_skew";
//preheader += m + "_kurt";
mfcc_statistics.Add(Statistics.Mean(mfccList[i]));
mfcc_statistics.Add(Statistics.Minimum(mfccList[i]));
mfcc_statistics.Add(Statistics.Variance(mfccList[i]));
mfcc_statistics.Add(Statistics.StandardDeviation(mfccList[i]));
mfcc_statistics.Add(Statistics.Median(mfccList[i]));
mfcc_statistics.Add(Statistics.LowerQuartile(mfccList[i]));
mfcc_statistics.Add(Statistics.UpperQuartile(mfccList[i]));
mfcc_statistics.Add(Statistics.Skewness(mfccList[i]));
mfcc_statistics.Add(Statistics.Kurtosis(mfccList[i]));
}
var td_statistics = new List <double>();
for (var i = 0; i < 4; i++)
{
td_statistics.Add(Statistics.Mean(tdList[i]));
td_statistics.Add(Statistics.Minimum(tdList[i]));
td_statistics.Add(Statistics.Variance(tdList[i]));
td_statistics.Add(Statistics.StandardDeviation(tdList[i]));
td_statistics.Add(Statistics.Median(tdList[i]));
td_statistics.Add(Statistics.LowerQuartile(tdList[i]));
td_statistics.Add(Statistics.UpperQuartile(tdList[i]));
td_statistics.Add(Statistics.Skewness(tdList[i]));
td_statistics.Add(Statistics.Kurtosis(tdList[i]));
}
// Write MFCCs
featureString += String.Join(",", mfcc_statistics);
featureString += ",";
featureString += String.Join(",", td_statistics);
//Write Spectral features as well
featureString += ",";
featureString += specFeatures;
Console.WriteLine($"{tag} Feature String ready {featureString}");
if (File.Exists(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp")))
{
File.Delete(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"));
File.WriteAllText(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"), featureString);
}
else
{
File.WriteAllText(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"), featureString);
}
MLContext mLContext = new MLContext();
string fileName = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp");
IDataView dataView = mLContext.Data.LoadFromTextFile <ModelInput>(
path: fileName,
hasHeader: false,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
// Use first line of dataset as model input
// You can replace this with new test data (hardcoded or from end-user application)
ModelInput sampleForPrediction = mLContext.Data.CreateEnumerable <ModelInput>(dataView, false)
.First();
ModelOutput opm = ConsumeModel.Predict(sampleForPrediction);
featureTimeList.Add(opm.Score);
Console.WriteLine($"{tag} Feature vs time list ready");
}
//Console.WriteLine($"{tag} MFCC: {mfccVectors.Count}");
//Console.WriteLine($"{tag} TD: {tdVectors.Count}");
//Console.WriteLine($"{tag} featureTimeArray: {featureTimeList.Count} {featureString}");
}
}
playAudio();
}
static void Main(string[] args)
{
DiscreteSignal signal;
// load
var mfcc_no = 24;
var samplingRate = 16000;
var mfccOptions = new MfccOptions
{
SamplingRate = samplingRate,
FeatureCount = mfcc_no,
FrameDuration = 0.025 /*sec*/,
HopDuration = 0.010 /*sec*/,
PreEmphasis = 0.97,
Window = WindowTypes.Hamming
};
var opts = new MultiFeatureOptions
{
SamplingRate = samplingRate,
FrameDuration = 0.025,
HopDuration = 0.010
};
var tdExtractor = new TimeDomainFeaturesExtractor(opts);
var mfccExtractor = new MfccExtractor(mfccOptions);
var folders = Directory.GetDirectories(Path.Combine(Environment.CurrentDirectory, "Dataset"));
Console.WriteLine($"Started!");
using (var writer = File.CreateText(Path.Combine(Environment.CurrentDirectory, "Data.csv")))
{
//Write header
var main_header = "genre,";
main_header += String.Join(",", mfccExtractor.FeatureDescriptions);
main_header += ",";
main_header += String.Join(",", tdExtractor.FeatureDescriptions);
main_header += ",centroid,spread,flatness,noiseness,roloff,crest,decrease,spectral_entropy";
writer.WriteLine(main_header);
string feature_string = String.Empty;
foreach (var folder in folders)
{
var f_name = new DirectoryInfo(folder).Name;
var files = Directory.GetFiles(Path.Combine(Environment.CurrentDirectory, "Dataset", folder));
//Write the genre label here
Console.WriteLine($"{f_name}");
foreach (var filename in files)
{
feature_string = String.Empty;
feature_string = $"{f_name},";
//MFCC
var avg_vec_mfcc = new List <float>(mfcc_no + 1);
//TD Features
var avg_vec_td = new List <float>(4);
//Spectral features
var avg_vec_spect = new List <float>(10);
for (var i = 0; i < mfcc_no; i++)
{
avg_vec_mfcc.Add(0f);
}
for (var i = 0; i < 4; i++)
{
avg_vec_td.Add(0f);
}
for (var i = 0; i < 10; i++)
{
avg_vec_spect.Add(0f);
}
string specFeatures = String.Empty;
using (var stream = new FileStream(Path.Combine(Environment.CurrentDirectory, "Dataset", filename), FileMode.Open))
{
var waveFile = new WaveFile(stream);
signal = waveFile[Channels.Average];
//Compute MFCC
tdVectors = tdExtractor.ComputeFrom(signal);
mfccVectors = mfccExtractor.ComputeFrom(signal);
var fftSize = 1024;
var fft = new Fft(fftSize);
var resolution = (float)samplingRate / fftSize;
var frequencies = Enumerable.Range(0, fftSize / 2 + 1)
.Select(f => f * resolution)
.ToArray();
var spectrum = new Fft(fftSize).MagnitudeSpectrum(signal).Samples;
var centroid = Spectral.Centroid(spectrum, frequencies);
var spread = Spectral.Spread(spectrum, frequencies);
var flatness = Spectral.Flatness(spectrum, 0);
var noiseness = Spectral.Noiseness(spectrum, frequencies, 3000);
var rolloff = Spectral.Rolloff(spectrum, frequencies, 0.85f);
var crest = Spectral.Crest(spectrum);
var decrease = Spectral.Decrease(spectrum);
var entropy = Spectral.Entropy(spectrum);
specFeatures = $"{centroid},{spread},{flatness},{noiseness},{rolloff},{crest},{decrease},{entropy}";
}
//Write label here TODO
foreach (var inst in mfccVectors)
{
for (var i = 0; i < mfcc_no; i++)
{
avg_vec_mfcc[i] += inst[i];
}
}
foreach (var inst in tdVectors)
{
for (var i = 0; i < 4; i++)
{
avg_vec_td[i] += inst[i];
}
}
for (var i = 0; i < mfcc_no; i++)
{
avg_vec_mfcc[i] /= mfccVectors.Count;
}
for (var i = 0; i < 4; i++)
{
avg_vec_td[i] /= tdVectors.Count;
}
// Write MFCCs
feature_string += String.Join(",", avg_vec_mfcc);
feature_string += ",";
feature_string += String.Join(",", avg_vec_td);
//Write Spectral features as well
feature_string += ",";
feature_string += specFeatures;
writer.WriteLine(feature_string);
var file_name = new DirectoryInfo(filename).Name;
Console.WriteLine($"{file_name}");
}
}
}
Console.WriteLine($"DONE");
Console.ReadLine();
}
