Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- audio-classification
+- math-rock
+- midwest-emo
+- mbti
+- tensorflow
+- keras
+metrics:
+- accuracy
+datasets:
+- anggars/neural-mathrock
+---
+
+# Neural Mathrock Classifier (v1.0)
+
+This model is a multi-output Custom Convolutional Neural Network (CNN) designed to analyze audio characteristics specifically within the Math Rock and Midwest Emo genres.
+
+## Model Outputs
+The architecture consists of five dedicated output heads, providing simultaneous classification for:
+1. **MBTI**: Personality type association based on musical patterns.
+2. **Emotion**: Emotional state detection (e.g., Fear, Sadness, Happiness).
+3. **Audio Vibe**: General atmosphere and sonic texture.
+4. **Intensity**: Aggression and energy levels.
+5. **Tempo**: Rhythmic speed classification (Slow, Medium, Fast).
+
+## Technical Specifications
+- **Input Shape**: 128x128x3 (Mel-Spectrograms)
+- **Framework**: TensorFlow 2.x / Keras 3
+- **Architecture**: Sequential CNN with Batch Normalization, Global Average Pooling, and Dropout layers for regularization.
+- **Optimization**: Adam Optimizer with Sparse Categorical Crossentropy loss for all heads.
+
+## Accuracy Performance
+Based on the final training logs (Epoch 20):
+- **Intensity/Tempo**: ~75-77%
+- **MBTI/Emotion**: ~55-63% (Outperforming baseline random classification for 16-class MBTI).
+
+## Files
+- `neural_mathrock_model.keras`: Trained weights and model architecture.
+- `neural_mathrock_labels.pkl`: Pickle file containing label mappings for decoding predictions.
+
+## Usage
+Preprocessing involves converting raw audio to Mel-Spectrograms at a sample rate of 22050 Hz, normalized to a 128x128 resolution. Use the provided pickle file to map the integer outputs to their respective categorical strings.