static void ANNFFNAccomp()
{
Databank db = new Databank("lib");
var cat = db.Load("Classical");
Composition inputComp = cat.Compositions[6];
MelodySequence inputSeq = inputComp.Tracks[0].GetMainSequence() as MelodySequence;
AccompanimentGeneratorANNFF gen = new AccompanimentGeneratorANNFF(cat, PatchNames.Acoustic_Grand);
//gen.Initialize();
gen.SetSequence(inputSeq);
gen.Train();
var outMel = gen.Generate();
MusicPlayer player = new MusicPlayer();
Composition comp = new Composition();
Track t = new Track(PatchNames.Orchestral_Strings, 6);
t.AddSequence(outMel);
comp.Add(t);
comp.Add(inputComp.Tracks[0]);
player.Play(comp);
comp.WriteToMidi("ann_ff_accomp.mid");
}
static void TrainANNFFAccomp()
{
Console.WriteLine("This application trains accompaniment neural networks");
var cats = Databank.GetCategories();
Databank db = new Databank("lib");
var popularInstruments = new PatchNames[]{PatchNames.Acoustic_Grand,PatchNames.String_Ensemble_1,
PatchNames.Acoustic_Bass,PatchNames.Trumpet,PatchNames.Vibraphone,PatchNames.Electric_Grand,
PatchNames.French_Horn,PatchNames.Flute,PatchNames.Trombone,PatchNames.Music_Box};
foreach (var catName in cats)
{
Console.WriteLine("Category {0}", catName);
var cat = db.Load(catName);
if (cat.Compositions.Length > 3000)
Console.WriteLine("Skipping category {0} due to length", cat.CategoryName);
foreach (var instr in popularInstruments)
{
Console.WriteLine("Training ANN for {0} - {1}, {2} epochs", cat.ToString(), instr.ToString(), 6000);
try
{
AccompanimentGeneratorANNFF ann = new AccompanimentGeneratorANNFF(cat, instr);
ann.Epochs = 5000;
ann.Train();
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
}
Console.ReadLine();
}
static void MetricTimingTest()
{
Databank db = new Databank("lib");
var cat = db.Load("Classical");
var fmets = new IMetric[]{new ChromaticTone(), new ChromaticToneDistance(), new ChromaticToneDuration(), new MelodicBigram(), new MelodicInterval()
, new Pitch(), new Rhythm(), new RhythmicBigram(), new RhythmicInterval()};
MusicPlayer player = new MusicPlayer();
foreach (var m in fmets)
{
Stopwatch watch = new Stopwatch();
watch.Start();
var metric = MetricSimilarity.GenerateMetricSimilarityMulti(cat.Compositions, new IMetric[] { m });
GeneticGenerator gen = new GeneticGenerator(metric);
gen.PrintProgress = false;
gen.MaxGenerations = 1000;
var mel = gen.Generate();
mel.Trim(20);
watch.Stop();
var className = ((object)m).GetType().Name;
Console.WriteLine("{0} - MF: {1}, AF {2}, T {3}", className, gen.MaxFitness, gen.AvgFitness, watch.ElapsedMilliseconds);
Console.ReadLine();
player.Play(mel);
}
Console.ReadLine();
}
static void MarkovAccompTest()
{
Databank db = new Databank("lib");
var cat = db.Load("Classical");
Composition inputComp = cat.Compositions[6];
MelodySequence inputSeq = inputComp.Tracks[0].GetMainSequence() as MelodySequence;
AccompanyGeneratorMarkov gen = new AccompanyGeneratorMarkov(cat, PatchNames.Orchestral_Strings);
var outMel = gen.Generate(inputSeq, 10);
MusicPlayer player = new MusicPlayer();
Composition comp = new Composition();
Track t = new Track(gen.Instrument, 6);
t.AddSequence(outMel);
comp.Add(t);
comp.Add(inputComp.Tracks[0]);
player.Play(comp);
comp.WriteToMidi("markov_model_accomp.mid");
}
static void Main(string[] args)
{
/*Feed forward neural network
Group notes into 3
Classify all groups in existing songs as 1, all other combinations as 0.
Use as fitness function
Use HMM decode as fitness function GA*/
/* Databank db = new Databank("lib");
List<MelodySequence> drumSeqs = new List<MelodySequence>();
var comps = new Composition[][] { db.Load("Classic Rock").Compositions, db.Load("Jazz").Compositions, Utils.LoadCompositionsParallel(@"D:\Sync\4th year\Midi\Library2\Drums") };
foreach(Composition[] catt in comps)
{
foreach(var c in catt)
{
foreach(var t in c.Tracks)
{
if(t.Channel == 10)
{
drumSeqs.Add(t.GetMainSequence() as MelodySequence);
}
}
}
}
List<Composition> drums = new List<Composition>();
foreach(var m in drumSeqs)
{
Composition c = new Composition();
Track t = new Track(PatchNames.Acoustic_Grand, 10);
t.AddSequence(m);
c.Add(t);
drums.Add(c);
}
var catdrum = new CompositionCategory("Drums", "lib/Drums", drums.ToArray());
catdrum.Save("lib/Drums");
Console.ReadLine();
*/
/*Composition comp = Composition.LoadFromMIDI(@"C:\Users\1gn1t0r\Documents\git\GeneticMIDI\GeneticMIDI\bin\Debug\test\other\twinkle.mid");
float time = Note.ToRealDuration(comp.GetLongestTrack().Duration);
Console.WriteLine("Total time: {0}", time);
MusicPlayer player = new MusicPlayer();
player.Play(comp);
Console.ReadLine();*/
var twink = Composition.LoadFromMIDI(@"C:\Users\1gn1t0r\Documents\git\GeneticMIDI\GeneticMIDI\bin\Debug\test\other\twinkle.mid");
Databank db = new Databank("lib");
var cat = db.Load("Jazz");
Codebook<Note> book = new Codebook<Note>();
List<int[]> sequences = new List<int[]>();
foreach(var comp in cat.Compositions)
{
foreach(var track in comp.Tracks)
{
if(track.Instrument == PatchNames.Trumpet)
{
var mel = track.GetMelodySequence();
mel.Trim(80);
mel.StandardizeDuration();
book.Add(mel.Notes);
sequences.Add(book.ToCodes(mel.Notes));
}
}
if (sequences.Count > 10)
break;
}
DotNetLearn.Markov.HiddenMarkovModel hmm = new DotNetLearn.Markov.HiddenMarkovModel(60, book.TotalUniqueSymbols);
hmm.UniformRandomPriors();
hmm.MaxIterations = 50;
hmm.Train(sequences.ToArray());
var o = hmm.PredictObservationSequence(80);
var newmel = new MelodySequence(book.Translate(o));
Track t = new Track(PatchNames.Trumpet, 2);
t.AddSequence(newmel);
Console.ReadLine();
MusicPlayer player = new MusicPlayer();
player.Play(t);
player.Stop();
Console.ReadLine();
/*
var gen = new SamplingWithReplacement(cat, PatchNames.Acoustic_Grand);
var mel = gen.Generate();
Console.WriteLine(mel.ToString());
MusicPlayer player = new MusicPlayer();
player.Play(mel);
Console.WriteLine("Press enter to save");
Console.ReadLine();
WriteMelodyToFile(gen.OriginalSequence, "sampling_in.mid");
WriteMelodyToFile(mel, "sampling_out.mid");
*/
/* MusicPlayer player = new MusicPlayer();
//db.LoadAll();
AccompanyGeneratorMarkov genMark = new AccompanyGeneratorMarkov(cat);
var targetSeq = cat.Compositions[2].GetLongestTrack().GetMainSequence() as MelodySequence;
var seqTest = genMark.Generate(targetSeq,5);
Track trackTest = new Track(PatchNames.Orchestral_Strings, 2); trackTest.AddSequence(seqTest);
Track targetTrack = new Track(PatchNames.Acoustic_Grand, 3); targetTrack.AddSequence(targetSeq);
Composition comp = new Composition();
comp.Add(trackTest);
comp.Add(targetTrack);
player.Play(comp);
return;*/
/* Databank db = new Databank("lib");
var cat = db.Load("Classical");
//AccompanimentGenerator2 Test
AccompanimentGenerator2 gen = new AccompanimentGenerator2(cat, PatchNames.Orchestral_Strings);
gen.Train();
//
Composition comp = Composition.LoadFromMIDI(@"D:\Sync\4th year\Midi\Library2\Classical\Mixed\dvorak.mid");
//var comp = Composition.LoadFromMIDI(@"C:\Users\1gn1t0r\Documents\git\GeneticMIDI\GeneticMIDI\bin\Debug\test\ff7tifa.mid");
gen.SetSequence(comp.Tracks[0].GetMainSequence() as MelodySequence);
MusicPlayer player = new MusicPlayer();
Console.WriteLine("Press enter to listen");
Console.ReadLine();
var mel = gen.Generate();
Composition newComp = new Composition();
Track newTrack = new Track(PatchNames.Orchestral_Strings , 2);
newTrack.AddSequence(mel);
newComp.Add(newTrack);
/*comp.Tracks[0].Instrument = PatchNames.Acoustic_Grand; (comp.Tracks[0].GetMainSequence() as MelodySequence).ScaleVelocity(0.8f);
/* newComp.Tracks.Add(comp.Tracks[0]);
player.Play(newComp);*/
Console.ReadLine();
}
static void InstrumentalTest()
{
Databank db = new Databank("lib");
var cat = db.Load("Classical");
InstrumentalGenerator gen = new InstrumentalGenerator(cat);
gen.Initialize();
var outMel = gen.GenerateInstrument(PatchNames.Acoustic_Grand, 40);
MusicPlayer player = new MusicPlayer();
player.Play(outMel);
Composition comp = new Composition();
Track t = new Track(PatchNames.Acoustic_Grand, 3);
t.AddSequence(outMel);
comp.Add(t);
comp.WriteToMidi("instrumental_acousticgrand.mid");
}
static void GetPopularInstruments()
{
Databank db = new Databank("lib");
var cat = db.Load("Classical");
var cat2 = db.Load("Jazz");
Dictionary<PatchNames, int> instrumentsCount = new Dictionary<PatchNames, int>();
foreach (var c in cat.Compositions)
{
foreach (var t in c.Tracks)
{
if (instrumentsCount.ContainsKey(t.Instrument))
instrumentsCount[t.Instrument]++;
else
instrumentsCount[t.Instrument] = 1;
}
}
foreach (var c in cat2.Compositions)
{
foreach (var t in c.Tracks)
{
if (instrumentsCount.ContainsKey(t.Instrument))
instrumentsCount[t.Instrument]++;
else
instrumentsCount[t.Instrument] = 1;
}
}
int n = 10;
var myList = instrumentsCount.ToList();
myList.Sort((firstPair, nextPair) =>
{
return firstPair.Value.CompareTo(nextPair.Value);
}
);
List<PatchNames> instrs = new List<PatchNames>();
for (int i = 0; i < n; i++)
{
instrs.Add(myList[myList.Count - i - 1].Key);
}
instrs.ToArray();
Console.Write("var pop = new PatchNames[]{");
foreach (var i in instrs)
Console.Write("PatchNames." + i.ToString() + ",");
Console.Write("};");
Console.ReadLine();
}
static void GeneticTest()
{
Databank db = new Databank("lib");
var cat = db.Load("Classical");
MetricSimilarity cosine = MetricSimilarity.GenerateMetricSimilarityMulti(cat.Compositions, new IMetric[] { new ChromaticToneDistance(), new MelodicInterval(), new ChromaticToneDuration(), new RhythmicBigram() });
GeneticGenerator gen = new GeneticGenerator(cosine, PatchNames.Orchestral_Strings, cat);
gen.OnPercentage += (sender, percentage, fitness) => { Console.WriteLine("{0}: {1}", percentage, fitness); };
gen.MaxGenerations = 1000;
var outMel = gen.Generate();
MusicPlayer player = new MusicPlayer();
player.Play(outMel);
Composition comp = new Composition();
Track t = new Track(gen.Instrument, 3);
t.AddSequence(outMel);
comp.Add(t);
comp.WriteToMidi("genetic_cosine_all.mid");
}
public void Train(Databank bank)
{
var cat = bank.Load("Drums");
int count = 0;
int percus = 0;
foreach (var c in cat.Compositions)
{
foreach (var t in c.Tracks)
{
if (t.Channel == 10)
{
var mel = t.GetMainSequence() as MelodySequence;
this.chain.Add(mel.ToArray());
percus++;
}
count++;
}
}
Save();
}
