diff --git a/KI-KNN b/KI-KNN
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+import librosa.feature
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from os import chdir
+import os
+import csv
+from tensorflow import keras
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from keras import models
+from keras import layers
+from keras import optimizers
+import matplotlib.pyplot as plt
+from sklearn.metrics import classification_report
+import constants
+
+
+max_len = 216
+
+
+def create_csv_header():
+ header = 'filename '
+ for i in range(constants.MFCC_FEATURE_START, ((constants.MFCC_FEATURE_END - 1) * max_len) + 1):
+ header += f' mfcc{i}'
+ header += ' label'
+ header = header.split()
+ file = open(constants.FEATURES_CSV_NAME, 'w', newline='')
+ with file:
+ writer = csv.writer(file)
+ writer.writerow(header)
+
+
+def extract_features(trainingDataDir, trainingDataSubDirs):
+ create_csv_header()
+ # Looping over every file inside the subdirectories for feature extraction
+ for trainingDataSubDir in trainingDataSubDirs:
+ for fileName in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
+ if fileName.endswith(".wav"):
+ audioFile = trainingDataDir/f'{trainingDataSubDir}/{fileName}'
+ print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audioFile.name)
+ y, sr = librosa.load(audioFile, mono=True)
+ mfcc = librosa.feature.mfcc(y=y, sr=44100, n_mfcc=constants.N_MFCC)
+ to_append = f'{audioFile.name}'
+
+ print ("mfcc.shape before cutting/padding: ", mfcc.shape)
+ #the mfcc extraction results in slighty different time frames per mfcc segment
+ #in the case of 5seconds and 20 mfcc segments there are som time informations between 215 and 217
+ #in order to unify the size the ones that stores under 216 timeinformation there will be some padding
+ #for the ones that stores over 216 time information there will be a trim operation
+ #IMPORTANT:
+ #if the time of the audiofiles or the mfcc extraction number changes (current value is 20)
+ #there needs to be an adjustment to the MAX_LEN parameter
+ #1. check the new size of all the files
+ #2. find a suitable average for padding or cutting
+ #3. adjust the MAX_LEN variable in the constants.py file
+ if (constants.MFCC_MAX_LEN > mfcc.shape[1]):
+ pad_width = max_len - mfcc.shape[1]
+ mfcc = np.pad(mfcc, pad_width=((0, 0), (0, pad_width)), mode='constant')
+ elif constants.MFCC_MAX_LEN < mfcc.shape[1]:
+ mfcc = mfcc[:, :constants.MFCC_MAX_LEN]
+ print ("mfcc.shape after cutting/padding: ", mfcc.shape)
+ #store every single value in the csv file
+ mfcc = np.reshape(mfcc, constants.MFCC_MAX_LEN * constants.N_MFCC)
+ #print ("new total size: ", mfcc.shape)
+ for g in mfcc:
+ to_append += f' {g}'
+ if trainingDataSubDir == constants.CAR:
+ to_append += f' {constants.LIGHT_WEIGHT}'
+ elif trainingDataSubDir == constants.BUS:
+ to_append += f' {constants.MEDIUM_WEIGHT}'
+ elif trainingDataSubDir == constants.TRUCK:
+ to_append += f' {constants.HEAVY_WEIGHT}'
+ elif trainingDataSubDir == constants.MOTORCYCLE:
+ to_append += f' {constants.TWO_WHEELED}'
+ elif trainingDataSubDir == constants.TRAM:
+ to_append += f' {constants.RAIL_BOUND}'
+
+ file = open(constants.FEATURES_CSV_NAME, 'a', newline='')
+ with file:
+ writer = csv.writer(file)
+ writer.writerow(to_append.split())
+
+def preprocessing_csv_data():
+ print("Reading Features... ")
+ data = pd.read_csv(constants.FEATURES_CSV_NAME)
+ data.head()
+ # Dropping unnecessary columns (Column Filename is dropped)
+ data = data.drop(['filename'], axis=1)
+ data.head()
+ return data
+
+
+def encode_labels(data):
+ # Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder
+ audio_list = data.iloc[:, -1]
+ encoder = LabelEncoder()
+ target_labels = encoder.fit_transform(audio_list)
+ return target_labels, encoder
+
+
+def normalize_data(data):
+ # normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
+ scaler = StandardScaler()
+ print (data.iloc[:, :-1])
+ X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
+ X = X.reshape(-1, constants.N_MFCC, constants.MFCC_MAX_LEN, constants.CHANNELS)
+ return X
+
+
+def train_test_data_split(X, y):
+ # splitting of dataset into train and test dataset
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
+ print (X_train[0].shape)
+ return X_train, X_test, y_train, y_test
+
+
+def create_and_compile_model():
+ print("Creating a Model")
+ # creating a model
+ from keras.models import Sequential
+ from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization
+ model = models.Sequential()
+ model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(constants.N_MFCC, constants.MFCC_MAX_LEN, constants.CHANNELS)))
+ model.add(Conv2D(32, kernel_size=(3, 3), activation="relu"))
+ model.add(MaxPooling2D(pool_size=(2,2)))
+ model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
+ model.add(MaxPooling2D(pool_size=(2,2)))
+ model.add(Dropout(0.5))
+ model.add(Flatten())
+ model.add(Dense(128, activation="relu"))
+ model.add(Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation='softmax'))
+ print("Compiling a Model")
+ optimizer = keras.optimizers.RMSprop()
+ model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
+ print(model.summary())
+ return model
+
+
+def train_and_save_model(model, X_train, y_train, X_test, y_test):
+ logdir = constants.LOG_DIR_PATH
+ tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
+ print("Start Training...")
+ history = model.fit(X_train, y_train, batch_size=32, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
+ # Saving the trained model to avoid re-training
+ model.save(constants.TRAINED_MODEL)
+ return history
+
+
+def predict(X_test, y_test):
+ print("Predictions.....")
+ predictions = np.argmax(model.predict(X_test), axis=-1)
+ target_names = [constants.LIGHT_WEIGHT, constants.MEDIUM_WEIGHT, constants.HEAVY_WEIGHT,constants.TWO_WHEELED, constants.RAIL_BOUND]
+ print(classification_report(y_test, predictions, target_names=target_names))
+
+
+def plot_model_accuracy(history):
+ # Plot graph Model Accuracy
+ plt.plot(history.history['accuracy'])
+ plt.plot(history.history['val_accuracy'])
+ plt.title('Model Accuracy')
+ plt.ylabel('Accuracy')
+ plt.xlabel('Epoch')
+ plt.legend(['Train', 'Test'], loc='upper left')
+ plt.show()
+
+
+def plot_model_loss(history):
+ # Plot graph Model Loss
+ plt.plot(history.history['loss'])
+ plt.plot(history.history['val_loss'])
+ plt.title('Model loss')
+ plt.ylabel('Loss')
+ plt.xlabel('Epoch')
+ plt.legend(['Train', 'Test'], loc='upper right')
+ plt.show()
+
+
+# Changing Directory to Training Dataset Folder
+chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
+trainingDataDir = Path.cwd()
+trainingDataSubDirs = os.listdir(trainingDataDir)
+chdir("..")
+if os.path.isfile(constants.FEATURES_CSV_NAME):
+ print("features.csv already exists, skip extraction")
+else:
+ extract_features(trainingDataDir, trainingDataSubDirs)
+#extract_features(trainingDataDir, trainingDataSubDirs)
+
+
+
+data = preprocessing_csv_data()
+target_labels, encoder = encode_labels(data)
+X = normalize_data(data)
+X_train, X_test, y_train, y_test = train_test_data_split(X, target_labels)
+model = create_and_compile_model()
+history = train_and_save_model(model, X_train, y_train, X_test, y_test)
+predict(X_test, y_test)
+plot_model_accuracy(history)
+plot_model_loss(history)
+