Bach or Bot

MusicLIME and Multimodal MLP Framework For Explainable Classification Of AI-Generated And Human-Composed Music
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Overview

A multimodal machine learning system that classifies music as human-composed or AI-generated using both audio features and lyrics analysis. This project implements a comprehensive pipeline combining state-of-the-art audio processing, spectro-temporal audio analysis, natural language processing, and explainable AI techniques to provide accurate and interpretable music classification.

Inspiration

Bach or Bot addresses the growing challenge of distinguishing between human-composed and AI-generated music in an era where artificial intelligence can create increasingly sophisticated musical content. The system employs a multimodal approach that analyzes both audio characteristics and lyrical content to make classification decisions, which provides explanations for its predictions through advanced explainability techniques.

Key Features

• Multimodal Classification: Combines audio and lyrics analysis for improved accuracy
• Audio-Only Classification: Supports classification and explanation using only audio features
• Explainable AI: Implements MusicLIME for interpretable model predictions
• RESTful API: FastAPI-based server with comprehensive endpoints
• Optimized Performance: Combined endpoints with shared processing for efficiency
• Scalable Architecture: Modular design supporting different model configurations

Architecture

The system follows a multimodal fusion architecture:

1. Audio and Lyrics Preprocessing: Raw audio files are resampled and normalized, while lyrics text is cleaned and tokenized for consistent input formatting
2. Audio Feature Extraction: SpecTTTra (Spectro-Temporal Tokens Transformer) extracts audio features from spectrograms
3. Lyrics Feature Extraction: LLM2Vec generates semantic embeddings from song lyrics
4. Feature Fusion: Intermediate fusion layer combines audio and text representations
5. Classification: Multi-layer perceptron (MLP) performs final binary classification
6. Explainability: MusicLIME provides feature-level explanations for predictions

Technical Implementation

• Audio Pipeline: Raw audio → Preprocessing → SpecTTTra feature extraction → Scaling → Classification
• Lyrics Pipeline: Raw text → Preprocessing → LLM2Vec embeddings → PCA reduction → Scaling → Classification
• Multimodal Fusion: Concatenated audio and lyrics features → MLP classifier
• Explainability: Source separation via OpenUnmix → Perturbation analysis → LIME explanations

Technologies and References

This project builds upon several state-of-the-art research contributions:

Core Models

SpecTTTra: Spectro-Temporal Tokens Transformer for audio representation learning

• Reference: Rahman, M. A., Hakim, Z. I. A., Sarker, N. H., Paul, B., & Fattah, S. A. (2024). "SONICS: Synthetic Or Not -- Identifying Counterfeit Songs." arXiv preprint arXiv:2408.14080. Accepted to ICLR 2025.
• Link: https://arxiv.org/abs/2408.14080
• Repository: https://github.com/awsaf49/sonics

LLM2Vec: Large Language Model to Vector conversion for text embeddings

• Reference: BehnamGhader, P., et al. (2024). "LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders." arXiv preprint arXiv:2404.05961.
• Link: https://arxiv.org/abs/2404.05961
• Repository: https://github.com/McGill-NLP/llm2vec

MusicLIME: Local Interpretable Model-agnostic Explanations for music classification

• Reference: Sotirou, T., et al. (2024). "MusicLIME: Explainable Multimodal Music Understanding." arXiv preprint arXiv:2409.10496. To be presented at ICASSP 2025.
• Link: https://arxiv.org/abs/2409.10496
• Repository: https://github.com/IamTheo2000/MusicLIME

Supporting Technologies

• Multi-Layer Perceptron (MLP): Neural network architecture for classification
• OpenUnmix: Source separation for audio factorization in explainability
• LIME: Local Interpretable Model-agnostic Explanations

Project Structure

bach-or-bot/
├── app/                            # FastAPI application
│ ├── schemas.py                    # Pydantic response models
│ ├── server.py                     # API endpoints and server configuration
│ ├── utils.py                      # Server utility functions
│ └── validators.py                 # Input validation functions
├── config/                         # Configuration files
│ ├── data_config.yml               # Data processing parameters
│ ├── model_config.yml              # Model hyperparameters
│ └── server_config.yml             # Server configuration
├── data/
│ ├── external/                     # External data and test samples
│ ├── processed/                    # Preprocessed datasets
│ └── raw/                          # Original datasets
├── docs/                           # Documentation
├── models/                         # Trained model artifacts
│ ├── fusion/                       # Fusion layer models and scalers
│ ├── mlp/                          # MLP classifier checkpoints
│ ├── musiclime/                    # MusicLIME model artifacts
│ └── spectttra/                    # SpecTTTra model checkpoints
├── notebooks/
│ ├── exploratory/                  # Data exploration and MusicLIME research
│ ├── inference/                    # Model inference notebooks
│ └── modeling/                     # Model development notebooks
├── scripts/                        # Execution scripts
│ ├── evaluate.py                   # Model evaluation
│ ├── explain.py                    # Explanation generation (multimodal, unimodal, combined)
│ ├── explain_runner.py             # Explanation testing script
│ ├── explain_combined_runner.py    # Combined explanation testing
│ ├── predict.py                    # Prediction pipeline (multimodal, unimodal, combined)
│ ├── predict_runner.py             # Prediction testing script
│ └── train.py                      # Training pipeline
├── src/                            # Source code modules
│ ├── llm2vectrain/                 # LLM2Vec training and inference
│ │ ├── model.py
│ │ ├── llm2vec_trainer.py
│ │ └── config.py
│ ├── models/                       # Model definitions
│ │ └── mlp.py                      # MLP classifier
│ ├── musiclime/                    # MusicLIME implementation
│ │ ├── explainer.py                # Core MusicLIME explainer
│ │ ├── factorization.py            # Audio source separation
│ │ ├── text_utils.py               # Text processing utilities
│ │ └── wrapper.py                  # Prediction wrappers
│ ├── preprocessing/                # Data preprocessing modules
│ │ ├── audio_preprocessor.py
│ │ ├── lyrics_preprocessor.py
│ │ └── preprocessor.py
│ ├── spectttra/                    # SpecTTTra implementation
│ │ ├── spectttra.py
│ │ ├── spectttra_trainer.py
│ │ ├── feature.py
│ │ ├── embedding.py
│ │ ├── tokenizer.py
│ │ └── transformer.py
│ └── utils/                        # Utility functions
├── tests/                          # Unit and integration tests
│ ├── test_preprocessing.py
│ ├── test_features.py
│ ├── test_mlp.py
│ ├── test_spectttra.py
│ └── test_musiclime.py
├── Dockerfile                      # Container configuration
├── Dockerfile.hf                   # Hugging Face deployment configuration
├── pyproject.toml                  # Project dependencies and metadata
└── README.md                       # This file!

Installation and Setup

Prerequisites

• Python 3.11 to 3.13 exclusively
• Poetry
• Git
• 8GB+ RAM recommended
• GPU recommended for faster inference (optional)

Installation

1. Clone the repository:

   git clone https://github.com/krislette/bach-or-bot.git
   cd bach-or-bot

2. Install dependencies using Poetry:

   poetry install

3. Activate the virtual environment:

   poetry env activate

Configuration

Environment Variables

• MUSICLIME_NUM_SAMPLES: Number of perturbation samples for LIME (default: 1000)
• MUSICLIME_NUM_FEATURES: Number of top features to return (default: 10)
• HF_TOKEN: Huggingface token for LLM2Vec access

Model Configuration

Edit config/model_config.yml to adjust model hyperparameters:

• MLP architecture (hidden layers, dropout, activation)
• Training parameters (learning rate, batch size, epochs)

Server Configuration

Edit config/server_config.yml to adjust server settings:

• File upload limits
• CORS settings
• API rate limiting

Usage

API Server

Start the FastAPI server:

poetry run uvicorn app.server:app --reload --host 0.0.0.0 --port 8000

The API will be available at http://localhost:8000 with interactive documentation at http://localhost:8000/docs.

Available Endpoints

Prediction Endpoints

Multimodal Prediction

• POST /api/v1/predict/multimodal - Prediction using both audio and lyrics
• POST /api/v1/predict - Legacy endpoint (same as multimodal)

Audio-Only Prediction

• POST /api/v1/predict/audio - Prediction using only audio features

Combined Prediction

• POST /api/v1/predict/combined - Both predictions in one call (convenience wrapper)

Explanation Endpoints

Multimodal Explanation

• POST /api/v1/explain/multimodal - MusicLIME explanation using both modalities
• POST /api/v1/explain - Legacy endpoint (same as multimodal)

Audio-Only Explanation

• POST /api/v1/explain/audio - MusicLIME explanation using only audio

Combined Explanation (Optimized

• POST /api/v1/explain/combined - Both explanations with shared source separation (~50% faster)

Information Endpoints

• GET / - API welcome message and endpoint listing
• GET /api/v1/model/info - Model information and capabilities

API Usage Examples

Multimodal Prediction

curl -X POST "http://localhost:8000/api/v1/predict/multimodal" \
 -H "Content-Type: multipart/form-data" \
 -F "audio_file=@path/to/song.mp3" \
 -F "lyrics=Your song lyrics here"

Response Format:

{
  "status": "success",
  "lyrics": "Your song lyrics here",
  "audio_file_name": "song.mp3",
  "audio_content_type": "audio/mpeg",
  "audio_file_size": 1234567,
  "results": {
    "confidence": 0.8542,
    "prediction": "Human-Composed",
    "label": 1,
    "probability": 0.8542
  }
}

Audio-Only Prediction

curl -X POST "http://localhost:8000/api/v1/predict/audio" \
 -H "Content-Type: multimodal/form-data" \
 -F "audio_file=@path/to/song.mp3"

Multimodal Explanation

curl -X POST "http://localhost:8000/api/v1/explain/multimodal" \
 -H "Content-Type: multipart/form-data" \
 -F "audio_file=@path/to/song.mp3" \
 -F "lyrics=Your song lyrics here"

Response Format:

{
  "status": "success",
  "lyrics": "Your song lyrics here",
  "audio_file_name": "song.mp3",
  "results": {
    "prediction": {
      "class": 1,
      "class_name": "Human-Composed",
      "confidence": 0.8542,
      "probabilities": [0.1458, 0.8542]
    },
    "explanations": [
      {
        "rank": 1,
        "modality": "audio",
        "feature_text": "vocals",
        "weight": 0.2341,
        "importance": 0.2341
      },
      {
        "rank": 2,
        "modality": "lyrics",
        "feature_text": "Line 1: Your song lyrics here",
        "weight": 0.1876,
        "importance": 0.1876
      }
    ],
    "summary": {
      "total_features_analyzed": 10,
      "audio_features_count": 6,
      "lyrics_features_count": 4,
      "runtime_seconds": 45.23,
      "samples_generated": 1000,
      "timestamp": "2024-10-26T09:15:30"
    }
  }
}

Combined Explanation (Optimized)

curl -X POST "http://localhost:8000/api/v1/explain/combined" \
 -H "Content-Type: multipart/form-data" \
 -F "audio_file=@path/to/song.mp3" \
 -F "lyrics=Your song lyrics here"

Response Format:

{
   "status": "success",
   "results": {
      "multimodal": {
         "prediction": { /_ multimodal prediction results / },
         "explanations": [ / multimodal explanations / ],
         "summary": { / multimodal processing summary / }
      },
      "audioonly": {
         "prediction": { /_ audio-only prediction results / },
         "explanations": [ / audio-only explanations / ],
         "summary": { / audio-only processing summary / }
      },
      "combinedsummary": {
         "total_runtime_seconds": 67.45,
         "factorization_time_seconds": 42.1,
         "source_separation_reused": true,
         "timestamp": "2024-10-26T09:15:30"
      }
   }
}

Command Line Usage

Running Predictions

Test multimodal and audio-only predictions:

poetry run python -m scripts.predict_runner

Running Explanations

Test explanation generation:

poetry run python -m scripts.explain_runner

Test combined predictions

poetry run python -m scripts.predict_combined_runner

Test combined explanations (optimized with shared source separation)

poetry run python -m scripts.explain_combined_runner

Model Training

Train the complete pipeline:

poetry run python -m scripts.train.py

Deployment

Build and run using Docker:

# Build the container
docker build -t bach-or-bot .

# Run the container
docker run -p 8000:8000 bach-or-bot

Hugging Face Spaces

The project includes a Hugging Face deployment configuration:

docker build -f Dockerfile.hf -t bach-or-bot-hf .

Model Performance

The system provides classification confidence scores and detailed explanations for each prediction. The multimodal approach typically achieves higher accuracy than audio-only classification by leveraging complementary information from both modalities.

Performance Optimizations

• Combined Explanations: The /explain/combined endpoint performs source separation once and reuses it for both multimodal and audio-only explanations which reduces processing time by approximately 50%
• Batch Processing: MusicLIME uses optimized batch processing for perturbation analysis
• Model Caching: Predictors load models once and reuse them across multiple requests

Explainability Features

• Feature Importance: Identifies which audio components and lyric lines contribute most to predictions
• Source Separation: Uses OpenUnmix to isolate different audio sources (vocals, drums, bass, other)
• Temporal Analysis: Analyzes audio features across different time segments
• Lyric Analysis: Highlights important lyrical phrases and semantic content
• Modality Comparison: Compare predictions and explanations between multimodal and audio-only approaches

Development

Adding Dependencies

Add runtime dependencies:

poetry add package-name

Add development dependencies:

poetry add --group dev package-name

Testing

Run the test suite:

poetry run pytest tests/

Run specific test modules:

poetry run pytest tests/test_musiclime.py
poetry run pytest tests/test_spectttra.py
poetry run pytest tests/test_mlp.py

Code Quality

The project follows Python best practices:

• PEP 8 style guidelines
• Comprehensive docstrings following NumPy/SciPy format
• Type hints where applicable
• Modular architecture for maintainability
• Separation of concerns between API, models, and utilities

Contributors

This project was developed by a collaborative team of researchers and developers:

• Acelle Krislette Rosales (acellekrislette@gmail.com) - MusicLIME implementation, explainability features, API and containerization, and final optimizations
• Hans Christian Queja (hansqueja8@gmail.com) - Preprocessing, SpecTTTra integration, training, and final optimizations
• Regina Bonifacio (bonifacioregina06@gmail.com) - MLP classifier architecture and training, and pytest testing
• Sean Matthew Sinalubong (s3amatth3wsinalubong@gmail.com) - LLM2Vec implementation and training
• Syruz Ken Domingo (syruzkenc.domingo@gmail.com) - SpecTTTra implementation and optimization

Citation

If you use this work in your research, please cite:

@software{bach_or_bot_2025,
   title = {Bach or Bot: MusicLIME and Multimodal MLP Framework For Explainable Classification Of AI-Generated And Human-Composed Music},
   author = {Rosales, Acelle Krislette and Queja, Hans Christian and Bonifacio, Regina and Sinalubong, Sean Matthew and Domingo, Syruz Ken},
   year = {2025},
   url = {https://github.com/krislette/bach-or-bot}
}

Acknowledgments

We acknowledge the research contributions that made this project possible, particularly the authors of SpecTTTra, LLM2Vec, and MusicLIME. This work builds upon their foundational research in audio processing, natural language processing, and explainable AI for music understanding.

Special thanks to the open-source community for providing the tools and libraries that enabled this research, including OpenUnmix for source separation, LIME for explainability frameworks, and the broader PyTorch ecosystem for deep learning infrastructure.

Contact

For questions or collaboration inquiries, please contact the development team through the repository issues page.

License

Distributed under the Apache 2.0 License. See LICENSE for more information.

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