Internet Music Expert Service
Artificial Intelligence version
IMES AI focuses on how copyright owners can allow AI to access music metadata and how NIM services can help monetize this access for copyright owners by leveraging advanced technology…
By leveraging NIM’s blockchain-based infrastructure and innovative services, copyright owners can receive fair compensation for using their songs’ metadata in AI-generated compositions.
The platform’s transparent tracking of metadata provenance, automated royalty calculation and distribution, and comprehensive reporting features ensure that copyright owners are rewarded for their intellectual property’s contribution to AI music generation.
- Audio Analysis: AI algorithms analyze raw audio to extract features defining musical content such as pitch, timbre, amplitude, tempo, rhythm, key, genre, mood, instrumentation, and song structure. Advanced deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are employed for these tasks.
- Text Processing: Natural Language Processing (NLP) techniques analyze textual information related to music, such as lyrics, titles, artist names, and album information. These techniques include Named Entity Recognition (NER), Sentiment Analysis, and Topic Modeling to structure and categorize metadata.
- Power of Content (PoC) Staking: Copyright owners can stake their copyrights and metadata on CopyrightChains, verifying ownership and authenticity. This staking generates rewards in NIM’s token economy, directly monetizing the metadata.
- Commercial Licensing and Royalties: The structured metadata is commercially licensable to AI companies for training music AI models. NIM services ensure proper tracking of music usage and fair royalty payments to copyright owners.
- Metadata as Intellectual Property: Structurally created metadata through AI music analysis is registered and anchored on the CopyrightChains, turning it into monetizable intellectual property assets within the NIM ecosystem.
- Metadata Provenance Recording: NIM’s blockchain-based services keep a detailed record of the metadata used to train AI models. Each time an AI model accesses and learns from a song’s metadata on the NIM platform, the usage is logged and associated with the respective copyright owner. This creates a transparent and immutable trail of metadata provenance, enabling the identification of the original songs that influenced the AI-generated composition.
- Smart Contract Integration: NIM utilizes smart contracts to automate the tracking and attribution of metadata usage in AI song generation. When an AI model generates a new song, the smart contract analyzes the provenance of metadata. It identifies the original songs and their corresponding copyright owners. The smart contract calculates the contribution of each original song’s metadata to the AI-generated composition based on predefined algorithms and weightage factors.
IMES’s AI services help music metadata owners discover and register previously unknown metadata, unlocking new opportunities for monetization and protection. Here’s how:
- Audio and Lyrical Analysis.
- Metadata Matching and Verification.
- Registration and Staking.
- Enhanced Discoverability and Monetization.
- Continuous Enrichment and Updates.
Copyright holders can uncover hidden value within their music catalogs, protect their intellectual property, and expand their monetization opportunities.
