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‎README.md

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This project features a comprehensive series of Jupyter notebooks documenting my iterative model development process:
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### 📓 [Base Model (29.7% Accuracy)](docs/notebooks/04_Base_Model.py)
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### 📓 [Base Model (29.7% Accuracy)](docs/notebooks/04_Base_Model.ipynb)
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My initial CNN-based approach established a strong baseline with:
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- Convolutional layers for feature extraction from mel spectrograms
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- Recurrent neural networks (GRU) for temporal sequence modeling
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- Basic data augmentation techniques for improved generalization
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- Identified key challenges for speech emotion recognition
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### 📓 [Enhanced Model (31.5% Accuracy)](docs/notebooks/05_Enhanced_Model.py)
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### 📓 [Enhanced Model (31.5% Accuracy)](docs/notebooks/05_Enhanced_Model.ipynb)
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Building on the base model, I incorporated:
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- Self-attention mechanisms to focus on emotionally salient parts of speech
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- Deeper convolutional blocks with residual connections
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- Improved regularization techniques including dropout and batch normalization
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- Advanced learning rate scheduling with cosine annealing
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### 📓 [Ultimate Model (33.3% Accuracy)](docs/notebooks/06_Ultimate_Model.py)
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### 📓 [Ultimate Model (33.3% Accuracy)](docs/notebooks/06_Ultimate_Model.ipynb)
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This complex architecture pushed the boundaries with:
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- Multi-modal feature extraction combining MFCCs, mel spectrograms, and spectral features
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- Full transformer architecture with multi-head self-attention
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- Squeeze-and-excitation blocks for channel-wise feature recalibration
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- Complex learning schedule with warmup and cosine annealing
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- 5-hour training time yielding only modest gains
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### 📓 [Simplified Model (50.5% Accuracy)](docs/notebooks/07_Simplified_Model.py)
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### 📓 [Simplified Model (50.5% Accuracy)](docs/notebooks/07_Simplified_Model.ipynb)
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My best-performing model proved that focused architectural design beats complexity:
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- Streamlined model with 4 transformer layers and 8 attention heads
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- Focused feature extraction with optimal dimensionality (256 features)
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- Convolutional layers for feature extraction
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- Simple recurrent layers for temporal modeling
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- Basic spectrogram features
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- Detailed in [04_Base_Model.py](docs/notebooks/04_Base_Model.py)
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- Detailed in [04_Base_Model.ipynb](docs/notebooks/04_Base_Model.ipynb)
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2. **Enhanced Model (31.5% accuracy)**
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- Added attention mechanisms for context awareness
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- Deeper convolutional feature extraction
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- Improved batch normalization strategy
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- Detailed in [05_Enhanced_Model.py](docs/notebooks/05_Enhanced_Model.py)
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- Detailed in [05_Enhanced_Model.ipynb](docs/notebooks/05_Enhanced_Model.ipynb)
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3. **Ultimate Model (33.3% accuracy)**
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- Full transformer architecture
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- Complex multi-head attention mechanisms
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- Advanced feature fusion techniques
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- Resource-intensive but limited generalization
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- Detailed in [06_Ultimate_Model.py](docs/notebooks/06_Ultimate_Model.py)
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- Detailed in [06_Ultimate_Model.ipynb](docs/notebooks/06_Ultimate_Model.ipynb)
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4. **Simplified Model (50.5% accuracy)** ✅
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- Focused architecture with 4 transformer layers
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- 8 attention heads with 256 feature dimensions
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- Robust error handling and training stability
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- Efficient batch processing with optimal hyperparameters
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- Detailed in [07_Simplified_Model.py](docs/notebooks/07_Simplified_Model.py)
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- Detailed in [07_Simplified_Model.ipynb](docs/notebooks/07_Simplified_Model.ipynb)
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The simplified model proved that architectural focus and training stability were more important than complexity for this task.
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‎docs/notebooks/00_Project_Overview.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# \ud83c\udfad Speech Emotion Recognition: Project Overview\n",
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"\n",
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"## Introduction\n",
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"\n",
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"This project documents the development of a deep learning system for recognizing emotions in human speech. Through iterative model development and architecture optimization, I achieved **50.5% accuracy** on an 8-class emotion recognition task using the RAVDESS dataset.\n",
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"\n",
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"This accuracy represents a significant achievement considering:\n",
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"- Random chance would be 12.5% for 8 classes\n",
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"- Commercial systems often focus on just 3-4 emotion classes\n",
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"- The nuanced differences between certain emotion pairs (e.g., neutral/calm)\n",
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"\n",
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"## Project Goals\n",
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"\n",
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"1. Develop a system capable of recognizing 8 distinct emotions from speech audio\n",
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"2. Explore different neural network architectures for audio processing\n",
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"3. Create a real-time inference system with intuitive visualization\n",
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"4. Document the development process and findings for educational purposes\n",
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"5. Achieve state-of-the-art performance on the RAVDESS dataset\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Documentation Structure\n",
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"\n",
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"This documentation is organized into the following notebooks:\n",
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"\n",
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"1. **Project Overview** (this notebook)\n",
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"2. **Dataset Exploration**: Understanding the RAVDESS dataset\n",
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"3. **Audio Feature Extraction**: Techniques for processing speech data\n",
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"4. **Base Model (29.7%)**: Initial CNN implementation\n",
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"5. **Enhanced Model (31.5%)**: Adding attention mechanisms\n",
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"6. **Ultimate Model (33.3%)**: Full transformer architecture\n",
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"7. **Simplified Model (50.5%)**: Optimized architecture with error handling\n",
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"8. **Model Comparison**: Analyzing performance across architectures\n",
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"9. **Real-time Inference**: Implementation of the emotion recognition GUI\n",
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"10. **Future Directions**: Areas for further improvement and research\n",
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"\n",
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"Each notebook contains detailed explanations, code implementations, visualizations, and analysis of results.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tech Stack\n",
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"\n",
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"This project utilizes the following technologies:\n",
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"\n",
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"- **Programming Language**: Python 3.8+\n",
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"- **Deep Learning Frameworks**: PyTorch 1.7+, TensorFlow 2.4+\n",
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"- **Audio Processing**: Librosa, PyAudio, SoundFile\n",
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"- **Data Science**: NumPy, Pandas, Matplotlib, scikit-learn\n",
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"- **Visualization**: TensorBoard, Matplotlib, Plotly\n",
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"- **GUI Development**: Tkinter\n",
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"- **Documentation**: Jupyter Notebooks\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Project Timeline\n",
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"\n",
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"The development of this project followed this timeline:\n",
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"\n",
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"1. **Initial Research and Dataset Selection** (Week 1)\n",
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"2. **Data Exploration and Preprocessing** (Week 2)\n",
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"3. **Base Model Development and Training** (Week 3)\n",
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"4. **Enhanced Model Architecture Design** (Week 4)\n",
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"5. **Ultimate Model Implementation** (Week 5)\n",
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"6. **Model Analysis and Error Diagnosis** (Week 6)\n",
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"7. **Simplified Model Design and Training** (Week 7)\n",
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"8. **Real-time Inference System Development** (Week 8)\n",
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"9. **Documentation and Code Refactoring** (Week 9-10)\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Results Preview\n",
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"\n",
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"| Model | Accuracy | F1-Score | Training Time | Key Features |\n",
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"|-------|----------|----------|---------------|-------------|\n",
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"| **Simplified (Best)** | **50.5%** | **0.48** | **~1h** | Error-resistant architecture, 4 transformer layers |\n",
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"| Ultimate | 33.3% | 0.32 | ~5h | Complex transformer architecture |\n",
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"| Enhanced | 31.5% | 0.30 | ~3h | Attention mechanisms |\n",
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"| Base | 29.7% | 0.28 | ~2h | Initial CNN implementation |\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Key Insights\n",
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"\n",
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"Through this project, I discovered several important insights about speech emotion recognition:\n",
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"\n",
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"1. **Architectural Simplicity**: More complex models don't always lead to better performance. The simplified model outperformed the more complex transformer architecture.\n",
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"\n",
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"2. **Error Handling Importance**: Robust error handling and training stability significantly improved model performance.\n",
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"\n",
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"3. **Feature Extraction**: Efficient audio preprocessing was crucial for good performance.\n",
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"\n",
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"4. **Emotion Confusion Patterns**: Certain emotion pairs are consistently confused (Happy/Surprised, Neutral/Calm).\n",
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"\n",
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"5. **Training Efficiency**: The simplified model trained in 1/5 the time of the ultimate model while achieving better results.\n",
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"\n",
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"These insights guided the final architecture design and helped achieve the 50.5% accuracy milestone.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## How to Use This Documentation\n",
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"\n",
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"Each notebook in this series is designed to be both educational and practical:\n",
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"\n",
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"- **Educational**: Detailed explanations of concepts, architecture decisions, and analysis of results\n",
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"- **Practical**: Executable code cells that you can run to reproduce results\n",
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"- **Visual**: Charts, diagrams, and visualizations to illustrate key concepts\n",
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"- **Progressive**: Building complexity from basic concepts to advanced implementations\n",
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"\n",
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"To get the most out of these notebooks:\n",
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"\n",
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"1. Follow the numbered sequence for a full understanding of the development process\n",
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"2. Run the code cells to see results in real-time\n",
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"3. Modify parameters to experiment with different configurations\n",
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"4. Refer to the project repository for the full codebase\n",
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"\n",
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"Let's begin exploring the fascinating world of speech emotion recognition! "
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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‎docs/notebooks/00_Project_Overview.py

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