Crafting the Future of Music: How Generative AI Platforms Like Beatoven.ai, AIVA, and Soundraw Are Made
Discover the future of music creation with generative AI platforms like Beatoven.ai, AIVA, and Soundraw. Our comprehensive guide reveals the intricate process behind building these innovative tools, detailing the essential architecture, technology stack, and team roles required. Learn how AI empowers users to compose personalized tracks effortlessly, blending cutting-edge technology with creativity.
Today, services and platforms using Generative AI models for Music Generation are gaining popularity. For example, let’s look at the platforms Beatoven.ai, AIVA (Artificial Intelligence Virtual Artist), Soundraw. Below is a comparative table of their features.
Each of these platforms has its strengths, making them suitable for different user needs and levels of expertise in music composition. However, their generalized functionality can be represented as follows.
Generalized functionality review (functional requirements):
Generating music using artificial intelligence.
The service uses artificial intelligence to create music tracks based on user settings such as genre, mood and duration.
users can select different genres (e.g., classical, jazz, electronic) and specify a mood (e.g., upbeat, soothing, dramatic).
the neural network generates visual covers for compositions
Free generation of up to N works;
By default, the service reserves the rights to the works; to obtain the rights to the works, you need to upgrade to a paid subscription.
If you have ever wondered what the architecture of such a system is, how to develop such a platform, what specialists are needed and how much time it will take, the article below reveals these points.
Requirements Gathering
Typically a project starts with a Requirements Gathering and with Definition of detailed product requirements and features. An important task is Gather input from stakeholders and potential users.
Let’s assume that as functional requirements (at the top level) we will focus on what was proposed above. And in addition to the functional requirements, it is also worth considering in the implementation of the system attention to non-functional requirements
Non-functional requirements:
1. Reliability
Availability of redundant and fault-tolerant solutions in the system architecture to ensure high availability and minimize downtime.
2 Availability
Distributed, load-balanced architecture to handle high traffic and ensure uninterrupted service.
Have a monitoring system in place to quickly detect and respond to system failures or performance bottlenecks.
3. Scalability
We use cloud infrastructure to scale the solution horizontally.
We use caching mechanisms to improve performance and reduce the load on server components.
Platform Architecture Design
After the functional and non-functional requirements are gathered and approved, the Platform Architecture is developed.
In the article format, let’s give an overview of the top-level architecture, without going into detail.
High-level Design (HLD)
1. User interface.
The user interface should be responsive, efficient to use, and visually appealing. It should provide easy navigation. This is the interface where users interact with the app to specify music options (genre, mood, duration), view created tracks, and manage their account. At the MVP stage, only the web version of the application is implemented.
2. Database
User Data: Stores user profiles, preferences, history, analytics, and account information.
Generated Music Metadata: Metadata about the generated music tracks (e.g. genre, mood, duration).
3. Music generation mechanism using artificial intelligence
Generative models: include artificial intelligence algorithms and models trained to generate music based on user data (genre, mood, etc.).
4. Audio storage
Storage system for efficient processing of large volumes of audio data. Dynamically expandable cloud-based big data storage solutions are used.
5. Processing and rendering
Audio Processing: Converts generated music data into playable audio formats (e.g. MP3, WAV). Renders music tracks in real time based on user interaction.
6. Content delivery and streaming
Uses a Content Delivery Network (CDN) to distribute audio worldwide and reduce network transmission latency. We use adaptive streaming protocols (such as HTTP (DASH) or HTTP Live Streaming (HLS)) to deliver audio depending on the users’ network conditions.
7. Recommendation mechanism
A recommendation system that offers users personalized audio content based on their preferences, browsing history and user behavior. Machine learning algorithms are used to analyze user data, audio metadata, and user interactions to make appropriate recommendations.
8. Analytics and reporting
Monitoring tools to track system performance, usage metrics, and user experience.
Usage Analytics: Tracks user behavior, preferences, and usage patterns to improve recommendations and user experience.
Reporting Dashboard: Provides platform usage information and collected statistics. Administrators have access to analytics and insights into performance, engagement, and monetization. Allows you to generate reports on user demographics, traffic sources, and content trends to help developers tailor the app to meet user needs.
9. Integration with external services
Royalty Free Music Database: Integrate with royalty free music databases or libraries for additional options or fallbacks.
Payment Gateway: Integration to process subscription payments or pay-as-you-go transactions.
10. Safety and compliance with legal requirements
Data Encryption: Provides encryption of sensitive user data and transactions in transit and at rest.
Access Control: Implements role-based access control (RBAC) to restrict access to sensitive operations and data.
Compliance: Complies with relevant data protection regulations and industry standards (e.g. GDPR, CCPA).
The top-level architecture of the audio content generation service is presented in the diagram:
Technology stack selection
Now that we have the architecture of the future service, we can select suitable technologies and solutions that will be used at the development stage. The list of technologies is presented in the table below.
This technology stack ensures a robust, scalable, and secure platform for AI-driven music generation, catering to the needs of users while providing flexibility for future enhancements.
Team build-up
Having an understanding of the top-level requirements, system architecture and technologies used, we are able to map the competencies that we will need to implement this service. The list of specialists we will engage to implement the music content creation service is shown in the following table.
Implementation Timeline
And ultimately, we draw up a schedule for development and testing and set the release date.
This timeline plan provides a structured approach to developing a complex AI-driven music generation platform. Adjustments may be needed based on project specifics, team size, and other factors. Regular check-ins and iterative development practices can help ensure the project stays on track and meets its goals.
Conclusion
In conclusion, the burgeoning field of generative AI for music composition offers immense potential, with platforms like Beatoven.ai, AIVA, and Soundraw leading the charge. Each platform brings unique features and capabilities, catering to various user needs and expertise levels. By leveraging advanced AI technologies, these platforms allow users to generate customized music tracks, providing a creative and efficient solution for music production.
To develop a robust AI-driven music generation platform, it is crucial to meticulously gather both functional and non-functional requirements, ensuring the system’s reliability, availability, and scalability. The high-level architecture encompasses a responsive user interface, secure and scalable databases, sophisticated music generation algorithms, efficient audio processing and storage, seamless content delivery, and personalized recommendation systems.
Implementing such a platform necessitates a comprehensive technology stack and a multidisciplinary team, including roles from product management to machine learning engineers, and from audio engineers to marketing specialists. This collaborative effort, guided by a well-defined implementation timeline, ensures the successful development, testing, and launch of the platform.
By adhering to this structured approach, we can build a scalable, secure, and user-friendly music generation platform that not only meets the current demands of users but also paves the way for future innovations in the realm of AI-driven music creation.
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