A modular AI-powered Audio Intelligence Platform.
SoundBrain is a modular artificial intelligence platform for professional audio analysis, understanding, reasoning, and recommendation.
Unlike traditional audio analyzers that only measure technical metrics, SoundBrain combines deterministic signal processing, machine learning, semantic embeddings, retrieval systems, and large language models into a unified architecture capable of understanding audio from both engineering and musical perspectives.
The project is designed around clean architecture principles where every subsystem has a single responsibility and can evolve independently.
Create one of the most complete open modular platforms for Audio Intelligence.
SoundBrain aims to become an engineering platform capable of:
- Audio Analysis
- Audio Understanding
- Semantic Audio Search
- Reference Matching
- Audio Reasoning
- AI Assisted Mixing
- AI Assisted Mastering
- Intelligent Recommendations
- Knowledge Retrieval
- Autonomous Audio Engineering
Audio Input
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Deterministic Measurement
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Engineering Analysis
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Audio Embeddings
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Context Builder
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Reference Retrieval
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Reasoning Engine
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Large Language Model
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Recommendation Engine
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Professional Report
- Modular Design
- Layer Isolation
- Dependency Injection
- Runtime Independence
- Provider Agnostic
- AI Model Abstraction
- Reproducible Results
- Testability
- Extensibility
Every component should have one responsibility.
Runtime never depends on domain logic.
Business logic never depends on model implementations.
Models are replaceable without changing the pipeline.
brain/
│
├── runtime/
├── audio/
├── embeddings/
├── intelligence/
├── reasoning/
├── reference/
├── recommendation/
├── memory/
├── rag/
├── reports/
├── llm/
├── services/
├── pipeline/
└── utils/
SoundBrain/
brain/
tests/
docs/
configs/
scripts/
data/
models/
pyproject.toml
requirements.txt
requirements-dev.txt
pytest.ini
README.md
- Modular Runtime
- Dynamic Model Loading
- Dependency Injection
- Audio Feature Extraction
- Loudness Analysis
- Spectral Analysis
- Semantic Audio Embeddings
- Similarity Search
- Reference Analysis
- RAG Integration
- LLM Reasoning
- Recommendation Engine
- Report Generation
Current architecture supports multiple providers.
Examples include:
- CLAP
- BGE
- Whisper
- Qwen
- Sentence Transformers
Additional providers can be integrated without modifying the Runtime.
- Production Ready
- Easily Extendable
- GPU Friendly
- CPU Compatible
- Clean APIs
- Fully Tested
- Architecture First
Clone the repository
git clone <repository-url>
cd SoundBrainCreate virtual environment
python -m venv .venvActivate
Windows
.venv\Scripts\activateLinux
source .venv/bin/activateInstall dependencies
pip install -r requirements-dev.txtpytestComplete project documentation is available inside the docs/ directory.
Main documents include:
- Architecture
- Vision
- Engineering Guidelines
- Roadmap
- Design Patterns
- Technical Debt
- Execution Plan
- Security
- Contribution Guide
SoundBrain follows an Architecture First development model.
Every new feature must satisfy the following principles:
- No circular dependencies
- Single Responsibility
- Layer Isolation
- Test Coverage
- Documentation
- Backward Compatibility
Implementation comes after architecture.
Current development focuses on building the core platform before advanced AI capabilities.
Major milestones include:
- Runtime
- Audio Intelligence
- Reference Intelligence
- Reasoning
- Recommendation
- Memory
- Knowledge
- Autonomous Engineering
MIT License
Hamid Haddadi
SoundBrain is an ongoing long-term engineering project focused on building a scalable, modular, and production-ready Audio Intelligence platform.