Update README.md

README.md

@@ -1,19 +1,80 @@
-
 ---
-language: id
+language:
+- en
+- id
+license: mit
 tags:
-- math-rock
-- mbti
 - multimodal
 - audio-classification
 - text-classification
+- math-rock
+- midwest-emo
+- emotion-recognition
+- personality-profiling
+- pytorch
+metrics:
+- f1
+- precision
+- recall
+- accuracy
 ---
 
-# Neural Mathrock MBTI Classifier
-Model multimodal (XLM-RoBERTa + CNN) buat klasifikasi MBTI dari lirik dan audio lagu Math Rock.
+# Neural Mathrock: Multimodal Emotion and Personality Analysis
+
+This repository hosts a multimodal deep learning framework specialized in the affective and psychological analysis of **Math Rock** and **Midwest Emo** music. By integrating lyrical semantics and acoustic patterns, the system provides a comprehensive profile of a track's emotional and personality-based characteristics.
+
+## Project Objectives
+
+The research is structured to prioritize emotional resonance and genre-specific complexities:
+
+1.  **Emotion Recognition:** Identifying affective states (e.g., Sadness, Tension, Joy) through the synergy of vocal delivery and lyrical themes.
+2.  **Personality (MBTI) Profiling:** Correlating complex musical arrangements and introspective lyrics with personality archetypes (e.g., INFP, INTJ, INTP).
+3.  **Acoustic Feature Extraction:** Analyzing technical attributes of Math Rock, including non-standard time signatures, syncopation, and clean guitar timbres.
+
+## Technical Architecture: Late Fusion Multimodal
+
+The system utilizes a **Late Fusion** approach to process distinct data modalities:
+
+### 1. Lyrical Stream (NLP)
+- **Encoder:** `xlm-roberta-base`
+- **Logic:** Extracts high-level semantic embeddings from song lyrics. The encoder is frozen to maintain stable pre-trained representations given the specialized nature of the dataset.
+
+### 2. Acoustic Stream (DSP)
+- **Model:** 1D-Convolutional Neural Network (CNN)
+- **Input:** 20-channel Mel-frequency cepstral coefficients (MFCC).
+- **Logic:** Captures the "twinkly" guitar textures and erratic drum patterns common in Midwest Emo and Math Rock.
+
+### 3. Fusion Layer
+- **Method:** Feature concatenation (768-dim Text + 256-dim Audio).
+- **Heads:** Multi-task fully connected layers for joint Emotion and Personality classification.
+
+## Performance Summary (Weighted Evaluation)
+
+The model was optimized using **Weighted Cross-Entropy Loss** to mitigate significant class imbalances.
+
+| Metric | Score |
+| :--- | :--- |
+| **Accuracy** | 0.81 |
+| **Weighted Avg F1** | 0.76 |
+| **Macro Avg F1** | 0.45 |
+
+### Class Highlights:
+- **INTJ:** 1.00 F1-score (Highly distinctive acoustic-lyrical signatures).
+- **INFP:** 0.89 F1-score (Robust detection of the genre's majority archetype).
+- **ISTP:** 0.53 F1-score (Successful identification of niche instrumental patterns).
+
+## Dataset
+
+Trained on the [anggars/neural-mathrock](https://huggingface.co/datasets/anggars/neural-mathrock) dataset, containing specialized annotations for emotion, MBTI, and musical features.
+
+## Academic Context
+
+This project is an undergraduate thesis developed at **Sekolah Tinggi Teknologi Cipasung (STTC)**, Informatics Department, Class of 2022. It explores the intersection of Music Information Retrieval (MIR) and psychological profiling.
+
+## How to Use
 
-### Status Model (Weighted 30 Epochs):
-- **Macro F1:** 0.45
-- **INTJ:** 1.00
-- **INTP:** 0.28
-- **ISTP:** 0.53
+```python
+import torch
+# Example: 
+# model.load_state_dict(torch.load("pytorch_model.bin"))
+# model.eval()