---
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.