diff --git a/.gitignore b/.gitignore
index f433a9a..40c76b3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,12 +1,19 @@
 
+## Logs
+src/logs/*
+
+## Trained Models
+src/models/trained_models/separation/*
+src/models/trained_models/transcription/*
+
 ## Datasets
 # Ignore all files in these specific directories
-/src/data/processed/test/model1/*
-/src/data/processed/test/model2/*
-/src/data/processed/train/model1/*
-/src/data/processed/train/model2/*
-/src/data/raw/model1/*
-/src/data/raw/model2/*
+src/data/processed/test/model1/*
+src/data/processed/test/model2/*
+src/data/processed/train/model1/*
+src/data/processed/train/model2/*
+src/data/raw/model1/*
+src/data/raw/model2/*
 
 # Except the directory structures
 !/src/data/processed/test/model1/.gitkeep
diff --git a/src/config/constants.py b/src/config/constants.py
index e091910..467ae2b 100644
--- a/src/config/constants.py
+++ b/src/config/constants.py
@@ -8,7 +8,7 @@
 # ----------
 
 # Model 1 Constants
-MODEL1_SUBSET = "V2"
+MODEL1_SUBSET = "V5"
 MODEL1_BASE_PATH = f"../data/raw/model1/{MODEL1_SUBSET}"
 MODEL1_TRAIN_FOLDER_PATH = "../data/processed/train/model1"
 MODEL1_TRAIN_FILE_NAME = f"mix_bass_train_data_{MODEL1_SUBSET}-TRAIN"
@@ -38,7 +38,11 @@
 TRAINED_MODEL2_SAVE_PATH = "../models/trained_models/transcription/mark1.pt"
 
 # Visualisation and Testing constants
-TRAINED_AUDIO_FILE_PATH = "../visualization/audio/trained_audio"
-PRED_AUDIO_FILE_PATH = "../visualization/audio/predicted_audio"
-VISUALIZATION_SAVE_PATH = "../visualization/spectrograms/outputs"
+TRAINED_AUDIO_FILE_PATH = (
+    "../../../../Bachelor Thesis/thesis/audio outputs/trained audio"
+)
+PRED_AUDIO_FILE_PATH = (
+    "../../../../Bachelor Thesis/thesis/audio outputs/predicted audio"
+)
+VISUALIZATION_SAVE_PATH = "../../../../Bachelor Thesis/thesis/spectrograms"
 PRED_MIDI_FILE_PATH = "../visualization/midi/predicted_midi"
diff --git a/src/config/fourierparameters.json b/src/config/fourierparameters.json
index 2434d58..465546d 100644
--- a/src/config/fourierparameters.json
+++ b/src/config/fourierparameters.json
@@ -1,8 +1,8 @@
 {
   "n_fft": 2048,
   "n_mels": 128,
-  "hop_length": 3308,
+  "hop_length": 512,
   "sample_rate": 44100,
   "track_seconds_considered": 30,
-  "audio_amplitude_threshold": 0.0001
+  "audio_amplitude_threshold": 0.0000001
 }
\ No newline at end of file
diff --git a/src/config/hyperparameters_separation.json b/src/config/hyperparameters_separation.json
index 0035fca..b29b499 100644
--- a/src/config/hyperparameters_separation.json
+++ b/src/config/hyperparameters_separation.json
@@ -1,7 +1,7 @@
 {
   "learning_rate": 0.0001,
-  "n_epochs": 10,
-  "hidden_dim": 512,
+  "n_epochs": 250,
+  "hidden_dim": 1024,
   "n_layers": 3,
-  "dropout_rate": 0.3
+  "dropout_rate": 0.1
 }
\ No newline at end of file
diff --git a/src/data_manipulation/__helpers__/normalization/decibel_normalizer.py b/src/data_manipulation/__helpers__/normalization/decibel_normalizer.py
new file mode 100644
index 0000000..a5a4484
--- /dev/null
+++ b/src/data_manipulation/__helpers__/normalization/decibel_normalizer.py
@@ -0,0 +1,38 @@
+import numpy as np
+
+
+class DecibelNormalizer:
+    def __init__(self, array):
+        self.array = array
+        self.min_val = None
+        self.max_val = None
+
+    def normalize(self):
+        # Convert spectrogram magnitudes to decibels
+        epsilon = 1e-10  # small constant to avoid log(0)
+        self.array = 20 * np.log10(np.maximum(self.array, epsilon))
+
+        # Find min and max values in the dB-scaled array
+        self.min_val, self.max_val = np.min(self.array), np.max(self.array)
+
+        # Avoid division-by-zero error in case of all same elements
+        if self.min_val == self.max_val:
+            self.array[:] = 0
+        else:
+            self.array = (self.array - self.min_val) / (self.max_val - self.min_val)
+
+        return self.array
+
+    def get_min_max(self):
+        return self.min_val, self.max_val
+
+    def denormalize(self, min_val, max_val):
+        if min_val is not None and max_val is not None:
+
+            # Apply the inverse of min-max normalization
+            self.array = (self.array * (max_val - min_val)) + min_val
+
+            # Convert decibels back to linear scale
+            self.array = 10 ** (self.array / 20.0)
+
+        return self.array
diff --git a/src/data_manipulation/for_production/production_audio_to_dict.py b/src/data_manipulation/for_production/production_audio_to_dict.py
index 12a4829..b2bfb7c 100644
--- a/src/data_manipulation/for_production/production_audio_to_dict.py
+++ b/src/data_manipulation/for_production/production_audio_to_dict.py
@@ -3,8 +3,8 @@
     convert_t_dict_key_to_numpy_arrays,
     convert_to_recarray,
 )
-from src.data_manipulation.__helpers__.normalization.mix_bass_data_normalizer import (
-    Normalizer,
+from src.data_manipulation.__helpers__.normalization.decibel_normalizer import (
+    DecibelNormalizer,
 )
 from src.transformers.audio_to_freq_time_analysis import audio_to_freq_time_analysis
 
@@ -36,8 +36,8 @@ def production_audio_to_dict(file_name, input_path):
         t_dict = convert_t_dict_key_to_numpy_arrays(dictionary=t_dict, keys=["x", "y"])
 
         # Normalize the data
-        norm_x = Normalizer(t_dict["x"])
-        t_dict["x"], t_dict["min_max_amplitudes"] = (
+        norm_x = DecibelNormalizer(t_dict["x"])
+        t_dict["x"], t_dict["x_min_max_amplitudes"] = (
             norm_x.normalize(),
             norm_x.get_min_max(),
         )
diff --git a/src/data_manipulation/for_training/audio_spectrograms/mixed_signal_to_dict.py b/src/data_manipulation/for_training/audio_spectrograms/mixed_signal_to_dict.py
index f94c024..0c006df 100644
--- a/src/data_manipulation/for_training/audio_spectrograms/mixed_signal_to_dict.py
+++ b/src/data_manipulation/for_training/audio_spectrograms/mixed_signal_to_dict.py
@@ -6,8 +6,8 @@
     savez_numpy_data,
     convert_to_recarray,
 )
-from src.data_manipulation.__helpers__.normalization.mix_bass_data_normalizer import (
-    Normalizer,
+from src.data_manipulation.__helpers__.normalization.decibel_normalizer import (
+    DecibelNormalizer,
 )
 from src.data_manipulation.__helpers__.truncator.mix_bass_data_truncator import (
     data_truncator,
@@ -111,16 +111,12 @@ def mixed_signal_to_dict(base_path, files_to_transform, save_file_path, pause=Fa
         t_dict = convert_t_dict_key_to_numpy_arrays(dictionary=t_dict, keys=["x", "y"])
 
         # Normalize the data
-        norm_x = Normalizer(t_dict["x"])
-        t_dict["x"], t_dict["min_max_amplitudes"] = (
-            norm_x.normalize(),
-            norm_x.get_min_max(),
-        )
-        norm_y = Normalizer(t_dict["y"])
-        t_dict["y"], t_dict["min_max_amplitudes"] = (
-            norm_y.normalize(),
-            norm_y.get_min_max(),
-        )
+        norm_x = DecibelNormalizer(t_dict["x"])
+        t_dict["x"] = norm_x.normalize()
+        t_dict["x_min_max_amplitudes"] = norm_x.get_min_max()
+        norm_y = DecibelNormalizer(t_dict["y"])
+        t_dict["y"] = norm_y.normalize()
+        t_dict["y_min_max_amplitudes"] = norm_y.get_min_max()
 
         # Transform to recarray
         t_dict_recarray = convert_to_recarray(data_dict=t_dict)
diff --git a/src/data_manipulation/for_training/tab_transcriptions/bass_and_midi_to_dict.py b/src/data_manipulation/for_training/tab_transcriptions/bass_and_midi_to_dict.py
index 7dc95c8..becea1f 100644
--- a/src/data_manipulation/for_training/tab_transcriptions/bass_and_midi_to_dict.py
+++ b/src/data_manipulation/for_training/tab_transcriptions/bass_and_midi_to_dict.py
@@ -5,8 +5,8 @@
     convert_to_recarray,
     savez_numpy_data,
 )
-from src.data_manipulation.__helpers__.normalization.mix_bass_data_normalizer import (
-    Normalizer,
+from src.data_manipulation.__helpers__.normalization.decibel_normalizer import (
+    DecibelNormalizer,
 )
 from src.data_manipulation.__helpers__.truncator.mix_bass_data_truncator import (
     data_truncator,
@@ -95,8 +95,8 @@ def bass_and_midi_to_dict(base_path, files_to_transform, save_file_path, pause=F
         t_dict = convert_t_dict_key_to_numpy_arrays(dictionary=t_dict, keys=["x", "y"])
 
         # Normalize the data
-        norm_x = Normalizer(t_dict["x"])
-        t_dict["x"], t_dict["min_max_amplitudes"] = (
+        norm_x = DecibelNormalizer(t_dict["x"])
+        t_dict["x"], t_dict["x_min_max_amplitudes"] = (
             norm_x.normalize(),
             norm_x.get_min_max(),
         )
diff --git a/src/main.py b/src/main.py
index ff99f79..415f4ec 100644
--- a/src/main.py
+++ b/src/main.py
@@ -15,80 +15,11 @@
 
 
 def main():
-    while True:
-        choice = (
-            input(
-                "@@@@@@ Would you like to create new models before processing your audio? @@@@@@\n"
-                '@@@@@@ WARNING: Selecting "yes" requires training/testing datasets to be present in the '
-                "data/raw/model1 and data/raw/model2 folders. @@@@@@\n"
-                '@@@@@@  Selecting "no" will use the pre-existing mark1 models instead. [Y/N]:'
-            )
-            .strip()
-            .lower()
-        )
 
-        if choice in ["yes", "y"]:
-            # Trains and tests both models separately
-            print()
-            print("@@@@@@ SEPARATION TRAINING/TESTING START @@@@@@")
-            separation_manager()
-            print()
-            print("@@@@@@ TRANSCRIPTION TRAINING/TESTING START @@@@@@")
-            transcription_manager()
-            print()
-            print("@@@@@@ MODELS SUCCESSFULLY CREATED @@@@@@")
-            break
-
-        elif choice in ["no", "n"]:
-            break
-
-        else:
-            print("Please enter a valid input. Either [Y/N] or [Yes/No].")
-
-    try:
-        input_folder = f"{PATH_TO_AUDIO}/{PRODUCTION_INPUT_FOLDER_PATH}"
-        if not os.listdir(input_folder):
-            print("No input files present in input folder. Programm terminated.")
-            return
-        for track in os.listdir(f"{input_folder}"):
-            if track.endswith(".wav"):
-                print()
-                print(f"@@@@ USING MODELS ON SELECTED TRACK: {track} @@@@")
-                input_file_path = (
-                    f"{PATH_TO_AUDIO}/{PRODUCTION_INPUT_FOLDER_PATH}/{track}"
-                )
-                output_file_path = (
-                    f"{PATH_TO_AUDIO}/{PRODUCTION_OUTPUT_FOLDER_PATH}/{track}"
-                )
-
-                print("@@ Pre-Processing... @@")
-                # Pre-process the audio into correct spectrogram format
-                processed_input = transform_data(
-                    flag="production input",
-                    file_name=track,
-                    input_file_path=input_file_path,
-                )
-
-                print("@@ Separating and transcribing... @@")
-                # Pass processed audio through both models
-                output = use_models_on_audio(processed_input)
-
-                print("@@ Post-Processing... @@")
-                # Post-process the midi-spectrogram into pure midi format and save it
-                piano_roll_to_midi(
-                    piano_roll=output,
-                    output_file_path=output_file_path,
-                    tag="",
-                    mix_names=["mix_name"],
-                    flag="production",
-                )
-
-            else:
-                print(f"@@@@ {track} is not a .wav file. Skipping... @@@@")
-    except Exception as e:
-        raise Exception(
-            f"An error occurred while processing the production audio files: {e}"
-        )
+    print("@@@@@@ SEPARATION TRAINING/TESTING START @@@@@@")
+    separation_manager()
+    print()
+    print("@@@@@@ MODEL SUCCESSFULLY CREATED @@@@@@")
 
 
 if __name__ == "__main__":
diff --git a/src/models/__helpers__/visualize_for_evaluation.py b/src/models/__helpers__/visualize_for_evaluation.py
deleted file mode 100644
index 54903c3..0000000
--- a/src/models/__helpers__/visualize_for_evaluation.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from src.config.constants import VISUALIZATION_SAVE_PATH, TRAINED_AUDIO_FILE_PATH
-from src.transformers.freq_time_analysis_to_audio import freq_time_analysis_to_audio
-from src.visualization.spectrograms.visualize_spectrograms import visualize_spectrograms
-
-
-def visualize_for_evaluation(outputs, x, y, data, flag, tag):
-    # Convert tensor back into numpy array
-    outputs_for_visualization = outputs.detach().cpu().numpy()
-    # Convert first three tracks back to audio for review
-    x_train_for_visualization = x.detach().cpu().numpy()
-    y_train_for_visualization = y.detach().cpu().numpy()
-    visualize_spectrograms(
-        VISUALIZATION_SAVE_PATH,
-        x_train_for_visualization[0],
-        y_train_for_visualization[0],
-        outputs_for_visualization[0],
-        data["mix_name"],
-        tag=tag,
-    )
-    if flag:
-        freq_time_analysis_to_audio(
-            mel_spectrogram_array=outputs_for_visualization[:3],
-            output_file_path=TRAINED_AUDIO_FILE_PATH,
-            mix_names=data["mix_name"],
-            min_max_amplitudes=data["min_max_amplitudes"],
-            tag=tag,
-        )
diff --git a/src/models/__helpers__/visualize_for_testing_evaluation.py b/src/models/__helpers__/visualize_for_testing_evaluation.py
new file mode 100644
index 0000000..af886b8
--- /dev/null
+++ b/src/models/__helpers__/visualize_for_testing_evaluation.py
@@ -0,0 +1,31 @@
+from src.config.constants import VISUALIZATION_SAVE_PATH
+from src.visualization.spectrograms.visualize_spectrogram import visualize_spectrogram
+
+
+def visualize_for_testing_evaluation(y_pred, y_test, x_test, data, tag):
+    # Convert tensor back into numpy array
+    y_pred_for_visualization = y_pred.detach().cpu().numpy()
+    x_test_for_visualization = x_test.detach().cpu().numpy()
+    y_test_for_visualization = y_test.detach().cpu().numpy()
+
+    visualize_spectrogram(
+        save_folder_path=VISUALIZATION_SAVE_PATH,
+        mel_spectrogram=x_test_for_visualization[0],
+        mix_name=data["mix_name"],
+        tag=tag,
+        label="x_test",
+    )
+    visualize_spectrogram(
+        save_folder_path=VISUALIZATION_SAVE_PATH,
+        mel_spectrogram=y_pred_for_visualization[0],
+        mix_name=data["mix_name"],
+        tag=tag,
+        label="y_pred",
+    )
+    visualize_spectrogram(
+        save_folder_path=VISUALIZATION_SAVE_PATH,
+        mel_spectrogram=y_test_for_visualization[0],
+        mix_name=data["mix_name"],
+        tag=tag,
+        label="y_test",
+    )
diff --git a/src/models/__helpers__/visualize_for_training_evaluation.py b/src/models/__helpers__/visualize_for_training_evaluation.py
new file mode 100644
index 0000000..081bd35
--- /dev/null
+++ b/src/models/__helpers__/visualize_for_training_evaluation.py
@@ -0,0 +1,41 @@
+from src.config.constants import VISUALIZATION_SAVE_PATH, TRAINED_AUDIO_FILE_PATH
+from src.transformers.freq_time_analysis_to_audio import freq_time_analysis_to_audio
+from src.visualization.spectrograms.visualize_spectrogram import visualize_spectrogram
+
+
+def visualize_for_training_evaluation(outputs, x, y, data, flag, tag):
+    # Convert tensor back into numpy array
+    outputs_for_visualization = outputs.detach().cpu().numpy()
+    x_train_for_visualization = x.detach().cpu().numpy()
+    y_train_for_visualization = y.detach().cpu().numpy()
+
+    visualize_spectrogram(
+        save_folder_path=VISUALIZATION_SAVE_PATH,
+        mel_spectrogram=x_train_for_visualization[0],
+        mix_name=data["mix_name"],
+        tag=tag,
+        label="x_train",
+    )
+    visualize_spectrogram(
+        save_folder_path=VISUALIZATION_SAVE_PATH,
+        mel_spectrogram=y_train_for_visualization[0],
+        mix_name=data["mix_name"],
+        tag=tag,
+        label="y_train",
+    )
+    visualize_spectrogram(
+        save_folder_path=VISUALIZATION_SAVE_PATH,
+        mel_spectrogram=outputs_for_visualization[0],
+        mix_name=data["mix_name"],
+        tag=tag,
+        label="y_output",
+    )
+
+    if flag:
+        freq_time_analysis_to_audio(
+            mel_spectrogram_array=outputs_for_visualization[:3],
+            output_file_path=TRAINED_AUDIO_FILE_PATH,
+            mix_names=data["mix_name"],
+            min_max_amplitudes=data["y_min_max_amplitudes"],
+            tag=tag,
+        )
diff --git a/src/models/audio_separation/gru/gru_separation.py b/src/models/audio_separation/gru/gru_separation.py
index 0c11b8f..4212252 100644
--- a/src/models/audio_separation/gru/gru_separation.py
+++ b/src/models/audio_separation/gru/gru_separation.py
@@ -15,25 +15,29 @@ def __init__(self, input_size, hidden_dim, n_layers, output_size, dropout_rate):
         # Dropout rate
         self.dropout_rate = dropout_rate
 
-        # GRU
-        self.gru = nn.GRU(input_size, hidden_dim, n_layers, batch_first=True)
+        # Bi-Directional GRU
+        self.gru = nn.GRU(
+            input_size, hidden_dim, n_layers, batch_first=True, bidirectional=True
+        )
 
         # Dropout layer
         self.dropout = nn.Dropout(dropout_rate)
 
         # Readout layers
-        self.fc1 = nn.Linear(hidden_dim, hidden_dim * 2)
-        self.fc2 = nn.Linear(hidden_dim * 2, hidden_dim * 4)
-        self.fc3 = nn.Linear(hidden_dim * 4, output_size)
+        self.fc1 = nn.Linear(self.hidden_dim * 2, self.hidden_dim * 2)
+        self.layer_norm1 = nn.LayerNorm(self.hidden_dim * 2)
+        self.fc2 = nn.Linear(self.hidden_dim * 2, self.hidden_dim * 4)
+        self.layer_norm2 = nn.LayerNorm(self.hidden_dim * 4)
+        self.fc3 = nn.Linear(self.hidden_dim * 4, output_size)
+
+        # Sigmoid activation for output bounding between 0 and 1.
+        self.sigmoid = nn.Sigmoid()
 
     def forward(self, x):
         """
         `x` is the batch of sequences that you want your RNN to process.
 
-        `x` has a shape of `(batch_size, seq_length, num_features)`:
-            - `batch_size` is the number of sequences you process at a time.
-            - `seq_length` is the length of each sequence.
-            - `num_features` is the number of input features at each sequence element.
+        `x` has a shape of `(batch_size, freq_bins, time_steps)`
         """
         batch_size = x.size(0)
 
@@ -47,15 +51,22 @@ def forward(self, x):
         out = self.dropout(out)
 
         out = self.fc1(out)
-        out = torch.relu(out)
+        out = torch.relu(self.layer_norm1(out.view(-1, out.size(2))))
+        out = out.view(batch_size, -1, self.hidden_dim * 2)
+
         out = self.fc2(out)
-        out = torch.relu(out)
+        out = torch.relu(self.layer_norm2(out.view(-1, out.size(2))))
+        out = out.view(batch_size, -1, self.hidden_dim * 4)
+
         out = self.fc3(out)
 
+        # Apply sigmoid activation to bound the outputs between 0 and 1
+        out = self.sigmoid(out)
+
         return out, hidden_j
 
     def init_hidden(self, batch_size):
         # Generates the first hidden state of zeros for the forward pass
-        hidden = torch.zeros(self.n_layers, batch_size, self.hidden_dim)
+        hidden = torch.zeros(self.n_layers * 2, batch_size, self.hidden_dim)
 
         return hidden
diff --git a/src/models/audio_separation/rnn/rnn.py b/src/models/audio_separation/rnn/rnn.py
index e9e1b4b..c260a34 100644
--- a/src/models/audio_separation/rnn/rnn.py
+++ b/src/models/audio_separation/rnn/rnn.py
@@ -32,10 +32,7 @@ def forward(self, x):
         """
         `x` is the batch of sequences that you want your RNN to process.
 
-        `x` has a shape of `(batch_size, seq_length, num_features)`:
-            - `batch_size` is the number of sequences you process at a time.
-            - `seq_length` is the length of each sequence.
-            - `num_features` is the number of input features at each sequence element.
+        `x` has a shape of `(batch_size, freq_bins, time_steps)`
         """
         batch_size = x.size(0)
 
diff --git a/src/models/audio_separation/separation_manager.py b/src/models/audio_separation/separation_manager.py
index 1728a9d..b2f07e6 100644
--- a/src/models/audio_separation/separation_manager.py
+++ b/src/models/audio_separation/separation_manager.py
@@ -16,6 +16,7 @@
     data_truncator,
 )
 from src.models.audio_separation.gru.gru_separation import GRU_Separation
+from src.models.audio_separation.rnn.rnn import RNN
 from src.models.test import test
 from src.models.train import train
 from src.transformers.freq_time_analysis_to_audio import freq_time_analysis_to_audio
@@ -30,8 +31,28 @@
 
 
 def separation_manager():
-    # Transform training/testing data for audio separation
-    transform_data(flag="audio separation")
+    while True:
+        choice = (
+            input(
+                "@@@@@@ Would you like to pre-process the data [1] or use the existing pre-processed data [2]?:"
+            )
+            .strip()
+            .lower()
+        )
+
+        if choice in ["1"]:
+            # Transform training/testing data for audio separation
+            print()
+            print("@@@@@@ DATA PRE-PROCESSING START @@@@@@")
+            transform_data(flag="audio separation")
+
+            break
+
+        elif choice in ["2"]:
+            break
+
+        else:
+            print("Please enter a valid input. Either [1] or [2].")
 
     # Load the training dataset
     print("@@@@ Loading the training dataset @@@@")
@@ -76,7 +97,7 @@ def separation_manager():
     y_test = y_test.float()
 
     # Initialize the model
-    model = GRU_Separation(
+    model = RNN(
         input_size=x_train.shape[2],
         hidden_dim=hyperparameters["hidden_dim"],
         n_layers=hyperparameters["n_layers"],
@@ -100,13 +121,13 @@ def separation_manager():
     torch.save(model.state_dict(), TRAINED_MODEL1_SAVE_PATH)
 
     # Test the model
-    y_pred = test(x_test, y_test, model, criterion)
+    y_pred = test(x_test, y_test, model, criterion, data_test, tag="separation")
 
     # Convert first three tracks back to audio for review
     freq_time_analysis_to_audio(
         mel_spectrogram_array=y_pred[:3],
         output_file_path=PRED_AUDIO_FILE_PATH,
         mix_names=data_test["mix_name"],
-        min_max_amplitudes=data_test["min_max_amplitudes"],
+        min_max_amplitudes=data_test["y_min_max_amplitudes"],
         tag="SEPARATION-TESTING",
     )
diff --git a/src/models/test.py b/src/models/test.py
index a213181..d9271af 100644
--- a/src/models/test.py
+++ b/src/models/test.py
@@ -1,4 +1,9 @@
-def test(x_test, y_test, model, criterion):
+from src.models.__helpers__.visualize_for_testing_evaluation import (
+    visualize_for_testing_evaluation,
+)
+
+
+def test(x_test, y_test, model, criterion, data, tag):
     print("@@@@ Starting model testing @@@@")
     # Switch the model to evaluation mode to turn off features like dropout
     model.eval()
@@ -9,6 +14,8 @@ def test(x_test, y_test, model, criterion):
     test_loss = criterion(y_pred, y_test)
     print(f"@@@@ Test loss: {test_loss.item():.7f} @@@@")
 
+    visualize_for_testing_evaluation(y_pred, y_test, x_test, data, tag)
+
     # Convert tensor back into numpy array
     y_pred = y_pred.detach().cpu().numpy()
 
diff --git a/src/models/train.py b/src/models/train.py
index 77b7f89..8405825 100644
--- a/src/models/train.py
+++ b/src/models/train.py
@@ -1,7 +1,12 @@
 import json
 import os
+import torch.optim.lr_scheduler as lr_scheduler
+from torch.utils.tensorboard import SummaryWriter
+import torch.nn.utils.clip_grad
 from src.models.__helpers__.learning_rate_reducer import learning_rate_reducer
-from src.models.__helpers__.visualize_for_evaluation import visualize_for_evaluation
+from src.models.__helpers__.visualize_for_training_evaluation import (
+    visualize_for_training_evaluation,
+)
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
 hyperparameters_separation_path = os.path.join(
@@ -17,6 +22,9 @@
 
 
 def train(x_train, y_train, model, criterion, optimizer, data_train, tag):
+    # TensorBoard Logging
+    writer = SummaryWriter(log_dir=os.path.join(dir_path, "../logs/tensorboard_logs/"))
+
     if tag == "separation":
         hyperparameters = hyperparameters_separation
     elif tag == "transcription":
@@ -24,8 +32,14 @@ def train(x_train, y_train, model, criterion, optimizer, data_train, tag):
     else:
         raise Exception("Incorrect tag")
 
-    # Track loss to break training loop if loss is no longer changing
+    # Create the lr scheduler
+    scheduler = lr_scheduler.ReduceLROnPlateau(
+        optimizer, "min", patience=5, factor=0.5, verbose=True
+    )
+
+    # Track loss to break training loop if loss is no longer changing or increasing
     no_change = 0
+    loss_increasing = False
     prev_loss = float("inf")
 
     # Track learning rate reduction
@@ -52,18 +66,31 @@ def train(x_train, y_train, model, criterion, optimizer, data_train, tag):
 
         # Backward pass (backpropagation) where gradients are calculated
         loss.backward()
+
+        # Gradient clipping
+        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.5)
+
         # Update model parameters, based on the gradients calculated in the backward pass
         optimizer.step()
 
+        # TensorBoard Logging
+        writer.add_scalar("Loss/train", loss.item(), epoch)
+
+        # Log parameter and gradient histograms
+        for name, param in model.named_parameters():
+            writer.add_histogram(f"Params/{name}", param, epoch)
+            if param.grad is not None:
+                writer.add_histogram(f"Grads/{name}", param.grad, epoch)
+
         # Print statistics
         print(f"@@ Epoch {epoch + 1} Done. loss: {loss.item():.7f} @@")
 
-        # Reduce learning rate if necessary
-        lr_reduced, optimizer = learning_rate_reducer(loss, optimizer, lr_reduced)
+        # Update the scheduler with the current loss
+        scheduler.step(loss.item())
 
         # Visualizations and audio-transforms for manual evaluation
         if epoch == hyperparameters["n_epochs"] - 1:
-            visualize_for_evaluation(
+            visualize_for_training_evaluation(
                 outputs,
                 x_train,
                 y_train,
@@ -78,19 +105,42 @@ def train(x_train, y_train, model, criterion, optimizer, data_train, tag):
         else:
             no_change = 0
 
+        # Check if the loss over the last 5 epochs has been larger than its previous loss
+        if loss.item() > prev_loss:
+            loss_increasing += 1
+        else:
+            loss_increasing = 0
+
         prev_loss = loss.item()
 
+        # If loss has started increasing, stop early
+        if loss_increasing >= 10:
+            print(
+                "@@@@@@ Stopping early - loss has increased too many times in a row. @@@@@@ "
+            )
+            visualize_for_training_evaluation(
+                outputs=outputs,
+                x=x_train,
+                y=y_train,
+                data=data_train,
+                tag=f"{tag}-TRAINING",
+                flag=True,
+            )
+            break
+
         # If loss hasn't changed for 30 epochs, stop early
         if no_change >= 30:
             print("@@@@@@ Stopping early - loss hasn't changed in 30 epochs. @@@@@@ ")
-            visualize_for_evaluation(
+            visualize_for_training_evaluation(
                 outputs,
                 x_train,
                 y_train,
                 data_train,
                 tag=f"{tag}-TRAINING",
-                flag=False,
+                flag=True,
             )
             break
 
+    writer.close()
+
     return model
diff --git a/src/models/trained_models/separation/mark1.pt b/src/models/trained_models/separation/mark1.pt
deleted file mode 100644
index c85617a..0000000
Binary files a/src/models/trained_models/separation/mark1.pt and /dev/null differ
diff --git a/src/models/trained_models/transcription/mark1.pt b/src/models/trained_models/transcription/mark1.pt
deleted file mode 100644
index f1137d7..0000000
Binary files a/src/models/trained_models/transcription/mark1.pt and /dev/null differ
diff --git a/src/models/use_models_on_audio.py b/src/models/use_models_on_audio.py
index a70b740..d5765f0 100644
--- a/src/models/use_models_on_audio.py
+++ b/src/models/use_models_on_audio.py
@@ -4,6 +4,7 @@
 
 from src.config.constants import SEPARATION_MODEL_PATH, TRANSCRIPTION_MODEL_PATH
 from src.models.audio_separation.gru.gru_separation import GRU_Separation
+from src.models.audio_separation.rnn.rnn import RNN
 from src.models.tab_transcription.gru.gru_transcription import GRU_Transcription
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -29,7 +30,7 @@ def use_models_on_audio(input_data):
     y_separation = y_separation.float()
 
     # Load trained separation model
-    separation_model = GRU_Separation(
+    separation_model = RNN(
         input_size=x_separation.shape[2],
         hidden_dim=hyperparameters_separation["hidden_dim"],
         n_layers=hyperparameters_separation["n_layers"],
diff --git a/src/scripts/rename_files.py b/src/scripts/rename_files.py
new file mode 100644
index 0000000..3d3c72e
--- /dev/null
+++ b/src/scripts/rename_files.py
@@ -0,0 +1,35 @@
+import os
+
+
+path = "../data/raw/model1/V2"
+
+
+folders = os.listdir(path)
+
+counter = 1
+# Iterate over each folder
+for folder in folders:
+    folder_path = os.path.join(path, folder)
+    # Rename mixture.wav file
+    mixture_file_path = os.path.join(folder_path, "mixture.wav")
+    if os.path.exists(mixture_file_path):
+        new_mixture_file_name = f"Track{counter}.wav"
+        new_mixture_file_path = os.path.join(folder_path, new_mixture_file_name)
+
+        # Rename the mixture.wav file
+        os.rename(mixture_file_path, new_mixture_file_path)
+        print(f"Renamed {mixture_file_path} to {new_mixture_file_path}")
+
+    # Rename Bass/Bass.wav file
+    bass_folder_path = os.path.join(folder_path, "Bass")
+    bass_file_path = os.path.join(bass_folder_path, "bass.wav")
+    if os.path.exists(bass_file_path):
+        new_bass_file_name = f"Bass{counter}.wav"
+        new_bass_file_path = os.path.join(bass_folder_path, new_bass_file_name)
+
+        # Rename the Bass.wav file
+        os.rename(bass_file_path, new_bass_file_path)
+        print(f"Renamed {bass_file_path} to {new_bass_file_path}")
+
+    # Increment the counter
+    counter += 1
diff --git a/src/transformers/audio_to_freq_time_analysis.py b/src/transformers/audio_to_freq_time_analysis.py
index 1de095c..3024c11 100644
--- a/src/transformers/audio_to_freq_time_analysis.py
+++ b/src/transformers/audio_to_freq_time_analysis.py
@@ -44,9 +44,12 @@ def audio_to_freq_time_analysis(file_path, flag=False):
         hop_length = fourierparameters["hop_length"]
         n_fft = fourierparameters["n_fft"]
         n_mels = fourierparameters["n_mels"]
+        win_length = fourierparameters["n_fft"]
 
         # Perform stft
-        stft = librosa.stft(signal, n_fft=n_fft, hop_length=hop_length)
+        stft = librosa.stft(
+            signal, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+        )
 
         # Calculate abs values on complex numbers to get magnitude
         mag_spectrogram, phase = librosa.magphase(stft)
diff --git a/src/transformers/freq_time_analysis_to_audio.py b/src/transformers/freq_time_analysis_to_audio.py
index 7c94898..5f2d534 100644
--- a/src/transformers/freq_time_analysis_to_audio.py
+++ b/src/transformers/freq_time_analysis_to_audio.py
@@ -3,8 +3,8 @@
 import json
 import os
 
-from src.data_manipulation.__helpers__.normalization.mix_bass_data_normalizer import (
-    Normalizer,
+from src.data_manipulation.__helpers__.normalization.decibel_normalizer import (
+    DecibelNormalizer,
 )
 from scipy.signal import butter, lfilter
 
@@ -40,10 +40,12 @@ def freq_time_analysis_to_audio(
         for track in range(mel_spectrogram_array.shape[0]):
             track_counter += 1
             min_val, max_val = min_max_amplitudes
-            mel_spectrogram_array[track] = Normalizer(
+
+            mel_spectrogram_array[track] = DecibelNormalizer(
                 mel_spectrogram_array[track]
             ).denormalize(min_val, max_val)
-            print(f"@@@@ Recreating audio of track {track} @@@@")
+
+            print(f"@@@@ Recreating audio of track {mix_names[track]} @@@@")
             spectrogram_array = librosa.feature.inverse.mel_to_stft(
                 mel_spectrogram_array,
                 sr=fourierparameters["sample_rate"],
@@ -57,10 +59,11 @@ def freq_time_analysis_to_audio(
                 hop_length=fourierparameters["hop_length"],
                 win_length=fourierparameters["n_fft"],
                 n_fft=fourierparameters["n_fft"],
+                n_iter=128,
             )
 
             audio = lowpass_filter(
-                audio, cutoff=1500, sr=fourierparameters["sample_rate"]
+                audio, cutoff=2500, sr=fourierparameters["sample_rate"]
             )
 
             # Save the audio to file
diff --git a/src/visualization/fourier_and_back_trial/fourier_audio_loss_trials.py b/src/visualization/fourier_and_back_trial/fourier_audio_loss_trials.py
index 05407b3..76bea4f 100644
--- a/src/visualization/fourier_and_back_trial/fourier_audio_loss_trials.py
+++ b/src/visualization/fourier_and_back_trial/fourier_audio_loss_trials.py
@@ -1,7 +1,7 @@
 import json
 import os
-from src.data_manipulation.__helpers__.normalization.mix_bass_data_normalizer import (
-    Normalizer,
+from src.data_manipulation.__helpers__.normalization.decibel_normalizer import (
+    DecibelNormalizer,
 )
 from src.transformers.audio_to_freq_time_analysis import audio_to_freq_time_analysis
 from src.transformers.freq_time_analysis_to_audio import freq_time_analysis_to_audio
@@ -11,7 +11,7 @@
 with open("../../config/fourierparameters.json") as fourierparameters_file:
     fourierparameters = json.load(fourierparameters_file)
 
-INPUT_FILE_PATH = "audio/MusicDelta_80sRock/Bass/MusicDelta_80sRock_STEM_02.wav"
+INPUT_FILE_PATH = "audio/Track56.wav"
 
 
 def fourier_audio_loss(file_path):
@@ -21,12 +21,12 @@ def fourier_audio_loss(file_path):
     """
     try:
         mel_spectrogram, phase = audio_to_freq_time_analysis(file_path)
-        t_dict = {"x": list(), "phase": list(), "min_max_amplitudes": list()}
+        t_dict = {"x": list(), "phase": list(), "x_min_max_amplitudes": list()}
 
         t_dict["x"].append(mel_spectrogram)
 
-        norm_x = Normalizer(t_dict["x"])
-        t_dict["x"], t_dict["min_max_amplitudes"] = (
+        norm_x = DecibelNormalizer(t_dict["x"])
+        t_dict["x"], t_dict["x_min_max_amplitudes"] = (
             norm_x.normalize(),
             norm_x.get_min_max(),
         )
@@ -34,11 +34,14 @@ def fourier_audio_loss(file_path):
 
         freq_time_analysis_to_audio(
             mel_spectrogram_array=t_dict["x"],
-            output_file_path="audio/",
-            mix_names=["MusicDelta_80sRock_MIX.wav"],
-            min_max_amplitudes=t_dict["min_max_amplitudes"],
-            tag="TRIAL-",
+            output_file_path="audio",
+            mix_names=["Track56.wav"],
+            min_max_amplitudes=t_dict["x_min_max_amplitudes"],
+            tag="TRIAL",
         )
 
     except Exception as e:
         raise Exception("Exception occurred: {}".format(e))
+
+
+fourier_audio_loss(INPUT_FILE_PATH)
diff --git a/src/visualization/spectrograms/visualize_spectrogram.py b/src/visualization/spectrograms/visualize_spectrogram.py
new file mode 100644
index 0000000..ac8b584
--- /dev/null
+++ b/src/visualization/spectrograms/visualize_spectrogram.py
@@ -0,0 +1,49 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import librosa
+import librosa.display
+import os
+import json
+
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+fourierparameters_path = os.path.join(dir_path, "../../config/fourierparameters.json")
+
+with open(fourierparameters_path) as fourierparameters_file:
+    fourierparameters = json.load(fourierparameters_file)
+
+
+def visualize_spectrogram(
+    save_folder_path,
+    mel_spectrogram,
+    mix_name,
+    tag,
+    label,
+):
+    """
+    Use this function to create graphs that help recognize the success of the separation model.
+    """
+
+    sr = fourierparameters["sample_rate"]
+    hop_length = fourierparameters["hop_length"]
+
+    print(f"@@@@ Creating {label} spectrogram @@@@")
+    plt.figure(figsize=(10, 4))
+    db_spectrogram = librosa.amplitude_to_db(
+        mel_spectrogram, ref=np.max(mel_spectrogram)
+    )
+    librosa.display.specshow(
+        db_spectrogram,
+        y_axis="mel",
+        fmax=8000,
+        x_axis="s",
+        sr=sr,
+        hop_length=hop_length,
+        vmin=np.min(db_spectrogram),
+        vmax=np.max(db_spectrogram),
+    )
+    plt.colorbar(format="%+2.0f dB")
+    plt.title(f"Mel spectrogram - {label} - {mix_name[0]}")
+    plt.tight_layout()
+    plt.savefig(f"{save_folder_path}/{tag}-spectrogram-{label}.png")
diff --git a/src/visualization/spectrograms/visualize_spectrograms.py b/src/visualization/spectrograms/visualize_spectrograms.py
deleted file mode 100644
index 5bbdcb7..0000000
--- a/src/visualization/spectrograms/visualize_spectrograms.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import librosa
-import librosa.display
-import os
-import json
-
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-
-fourierparameters_path = os.path.join(dir_path, "../../config/fourierparameters.json")
-
-with open(fourierparameters_path) as fourierparameters_file:
-    fourierparameters = json.load(fourierparameters_file)
-
-
-def visualize_spectrograms(
-    save_folder_path,
-    mel_spectrogram_x_train,
-    mel_spectrogram_y_train,
-    mel_spectrogram_y_train_output,
-    mix_name,
-    tag,
-):
-    """
-    Use this function to create graphs that help recognize the training success of the separation model.
-    """
-
-    sr = fourierparameters["sample_rate"]
-    hop_length = fourierparameters["hop_length"]
-
-    print("@@@@ Creating x_train spectrogram @@@@")
-    plt.figure(figsize=(10, 4))
-    librosa.display.specshow(
-        librosa.power_to_db(mel_spectrogram_x_train, ref=np.max),
-        y_axis="mel",
-        fmax=8000,
-        x_axis="time",
-        sr=sr,
-        hop_length=hop_length,
-    )
-    plt.colorbar(format="%+2.0f dB")
-    plt.title(f"Mel spectrogram - x_train - {mix_name[0]}")
-    plt.tight_layout()
-    plt.savefig(f"{save_folder_path}/{tag}-spectrogram-x_train.png")
-
-    print("@@@@ Creating y_train spectrogram @@@@")
-    plt.figure(figsize=(10, 4))
-    librosa.display.specshow(
-        librosa.power_to_db(mel_spectrogram_y_train, ref=np.max),
-        y_axis="mel",
-        fmax=8000,
-        x_axis="time",
-        sr=sr,
-        hop_length=hop_length,
-    )
-    plt.colorbar(format="%+2.0f dB")
-    plt.title(f"Mel spectrogram - {mix_name[0]}")
-    plt.tight_layout()
-    plt.savefig(f"{save_folder_path}/{tag}-spectrogram-y_train.png")
-
-    print("@@@@ Creating y_train_output spectrogram @@@@")
-    plt.figure(figsize=(10, 4))
-    librosa.display.specshow(
-        librosa.power_to_db(mel_spectrogram_y_train_output, ref=np.max),
-        y_axis="mel",
-        fmax=8000,
-        x_axis="time",
-        sr=sr,
-        hop_length=hop_length,
-    )
-    plt.colorbar(format="%+2.0f dB")
-    plt.title(f"Mel spectrogram - {mix_name[0]}")
-    plt.tight_layout()
-    plt.savefig(f"{save_folder_path}/{tag}-spectrogram-y_train_output.png")