DeepMusicLab aims to integrate an artificial intelligence driven music service, facilitating ease of deployment and integration for developers and music enthusiasts alike. This platform serves as a comprehensive toolkit for exploring and applying cutting-edge music AI technologies, offering functionalities such as automatic music tagging, style identification, sentiment analysis, and recommendation systems.
- Automated extraction of key musical features including tempo, pitch, and harmony.
- Application of deep learning models to identify and classify various music genres and styles.
- Analysis of the emotional content of music to support personalized music recommendations and curated playlists.
- Intelligent music suggestions based on user preferences and musical characteristics.
- Utilization of AI algorithms to generate new musical pieces or melodies.
- Provision of straightforward APIs and tools for seamless integration into existing applications.
- Support for diverse deployment options, including cloud and on-premises servers.
- Music Apps: Enhance the intelligence of music players with advanced recommendation features.
- Music Production: Assist music producers in creating new tracks or remixes.
- Music Education: Provide tools for music theory teaching and melody composition.
- Music Analysis: Offer in-depth analytical tools for music researchers.
- Deep Learning Frameworks: TensorFlow, PyTorch
- Programming Languages: Python, JavaScript (Node.js)
- Databases: SQLite, MongoDB
- API Interface: RESTful API
We welcome contributions in any form, including code, documentation, and design suggestions. Please refer to our Contribution Guidelines to learn how to get involved.
DeepMusicLab is open-source software, released under the MIT License.
Our gratitude goes to all developers and users who have contributed to the DeepMusicLab project.