The music industry faced a crisis in 2023 when a fake duet featuring Drake and The Weeknd went viral, revealing the challenge of AI-generated music. This incident prompted the industry to develop technologies to trace and manage AI-generated songs. Rather than focusing on banning such content, the industry is now concentrating on monetizing it.
Various detection systems are being integrated into the music pipeline, including tools for model training, uploading platforms, rights licensing databases, and discovery algorithms. The objective is not just to identify synthetic content after its release but to track it early, add metadata, and regulate its movement through the system. Matt Adell, co-founder of Musical AI, emphasizes the importance of incorporating such infrastructure from training to distribution stages.
Startups are emerging to embed detection mechanisms into licensing processes. Companies like YouTube, Deezer, Audible Magic, Pex, Rightsify, and SoundCloud are enhancing their systems to identify, moderate, and attribute AI-generated content across various stages from training datasets to distribution platforms.
Some companies are going a step further by developing tools to tag AI music during its creation. Firms like Vermillio and Musical AI are working on systems to scan tracks for synthetic elements, enabling rights holders to detect mimicry at a granular level and facilitate proactive licensing and authenticated release.
By analyzing model training data, some companies aim to estimate the level of influence from specific artists or songs in generated tracks. This approach could lead to more precise licensing arrangements based on creative influences, thereby avoiding post-release disputes.
Deezer has implemented internal tools to flag fully AI-generated tracks upon upload and reduce their visibility in recommendations, particularly when the content appears spammy. The company plans to label such tracks directly for users to maintain transparency and combat misuse of the platform.
The introduction of AI’s Do Not Train Protocol (DNTP) allows artists and rights holders to mark their work as off-limits for model training. While visual artists have similar tools, the audio sector is still catching up. Standardizing consent, transparency, and licensing remains a challenge, with ongoing discussions on the need for regulation and industry-wide adoption.
Although the music industry is embracing AI technology, concerns persist regarding its misuse for exploitation. The industry’s focus on developing detection and attribution systems reflects a proactive approach to managing AI-generated content and ensuring fair compensation for creators and rights holders.
As the industry continues to evolve, the integration of AI detection tools into various stages of music production and distribution signifies a shift towards a more transparent and regulated environment, where creative influence is quantified, and artists are protected from unauthorized use of their work.
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