Init visualization, Add compined wav test file

classifier/feature_extraction/feature_extractor.py

@@ -1,4 +1,3 @@
-# import essentia.standard
 import essentia
 from essentia.standard import (MonoLoader, Windowing, Spectrum, MFCC,
 	ZeroCrossingRate, SpectralCentroidTime, RollOff, Flux, Envelope,

classifier/pipeline.py

@@ -1,7 +1,8 @@
 import numpy as np
+from feature_extraction.feature_extractor import extractFeatures
 from feature_extraction.batch_feature_extractor import batchExtract
-from preprocessing.data_preprocessing import createSingleFeaturesArray, standardization, PCA
+from preprocessing.data_preprocessing import arrayFromJSON, createSingleFeaturesArray, standardization, PCA
-from classification_model_training.model_training import simpleTrain, kFCrossValid
+from training.model_training import simpleTrain, kFCrossValid
 
 batchExtract('../dataset/music_wav/', 'feature_extraction/music_features/', 22050)
 batchExtract('../dataset/speech_wav/', 'feature_extraction/speech_features/', 22050)
@@ -14,4 +15,10 @@ dataset = standardization(dataset)
 # dataset = PCA(dataset)
 print('Simple train accuracy achieved = ' + str(simpleTrain(dataset, target)))
 kFCrossValid(dataset, target, model = 'svm')
-kFCrossValid(dataset, target, model = 'rndForest')
+clf = kFCrossValid(dataset, target, model = 'rndForest')
+
+extractFeatures('compined.wav', 'featuresStream/tmp.json', 22050)
+values = arrayFromJSON('featuresStream/tmp.json')[1]
+values = standardization(values)
+audioClass = clf.predict(values)
+print(audioClass)

classifier/visualization/visualization.py

@@ -0,0 +1,17 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set()
+
+datasetArray = np.load('../preprocessing/dataset.npy')
+target = np.load('../preprocessing/target.npy')
+featureKeysVector = np.load('../preprocessing/featureKeys.npy')
+
+dataset = pd.DataFrame(datasetArray)
+dataset.columns = featureKeysVector
+
+sns.relplot(x="4HzMod", y="Flat", data=dataset[["4HzMod", "Flat"]], hue = target, style = target)
+sns.jointplot(x="SLAtt", y="ZCR", data=dataset[["SLAtt", "ZCR"]]);
+sns.pairplot(data=dataset[["SDec", "Flat", "mfcc2", "HFC"]]);
+plt.show()