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+3. Data Problem
+This document outlines the specific instructions for preparing the provided database of human voice
+recordings for training a machine learning model capable of distinguishing between authentic and
+synthetic voices.
+1. Data Exploration and Analysis:
+Utilize tools such as Matplotlib and Seaborn for in-depth data analysis and visualization.
+Begin with a comprehensive exploration of the database, understanding characteristics, and
+assessing the distribution of authentic and synthetic samples.
+ Identify and address imbalanced samples in the dataset.
+2. Imbalance Handling:
+
+
+
+Enhance model performance by employing techniques such as oversampling or undersampling,
+e.g., using SMOTE or Imblearn.
+3. Data Cleaning:
+
+
+ Address variations in sample wav length by finding the mean of total sample lengths.
+ Utilize padding techniques to standardize each sample to the fixed mean length.
+ Handle misclassified samples within the dataset.
+4. Feature Engineering:
+ Extract relevant acoustic features like MFCCs, spectrograms, and pitch from audio recordings.
+ Experiment with different feature sets to identify the most discriminative ones.
+ Normalize and standardize features for consistent scaling, facilitating model training.
+5. Speaker Embeddings:
+Consider incorporating speaker embeddings to capture individual characteristics, enhancing the
+model's ability to generalize across diverse voices.
+ Implement suitable methods for extracting speaker embeddings, such as pre-trained models or
+training on the dataset.
+6. Data Splitting:
+
+
+Split the data into training, validation, and test sets, ensuring a stratified split.
+Evaluate model performance on the validation set, minimizing loss before final testing on the
+test samples.
+7. Data Augmentation:
+
+
+
+Apply data augmentation techniques to increase model robustness against variations in
+recording conditions.
+ Techniques may include random pitch shifts, time-stretching, or introducing background noise.
+8. Quality Control:
+
+
+
+
+
+Conduct a rigorous quality control check to identify and address anomalies or outliers in the
+dataset.
+Verify that data preprocessing steps do not introduce artifacts negatively affecting model
+performance.
+
+Once the data is prepared following these guidelines, the transition into the model development
+phase will focus on selecting an appropriate architecture, training the model, and fine-tuning it for
+optimal performance.
+
+