{"id":9734,"date":"2026-09-20T18:21:38","date_gmt":"2026-09-20T16:21:38","guid":{"rendered":"https:\/\/tunelockr.com\/?page_id=9734"},"modified":"2026-09-20T18:21:38","modified_gmt":"2026-09-20T16:21:38","slug":"licence-musique-intelligence-artificielle","status":"publish","type":"page","link":"https:\/\/tunelockr.com\/en\/licence-musique-intelligence-artificielle\/","title":{"rendered":"Music licensing for training artificial intelligence"},"content":{"rendered":"
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TuneLockr Guide \u00b7 AI & rights<\/span><\/p>\n

Music licensing for training artificial intelligence<\/h1>\n

Allowing AI-related usage requires defining the data, intended purposes, relevant models, permitted outputs and economic terms.<\/p>\n

Define AI permissions \u2197<\/a>See the main guide<\/a><\/div>\n

First registration free \u00b7 no bank card required<\/p>\n<\/header>\n

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Artificial intelligence music licence<\/strong> here refers to a concrete process: identifying the correct rights holders, defining the intended use or evidence required, formalising decisions and keeping the elements that will help understand the agreement later. This method avoids vague promises and reduces misunderstandings between artists, producers, labels, publishers, and users.<\/p>\n

TuneLockr helps creators organise their files, evidence, and permissions. The tool does not replace a collecting society or personalised legal advice. The information below is educational and should be tailored to the project, territory, and any existing contracts.<\/p>\n

Artificial intelligence music licence: the four key decisions<\/h2>\n
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Data covered<\/h3>\n

Master, composition, stems, vocals, lyrics, metadata and annotations must be distinguished.<\/p>\n<\/article>\n

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Purpose<\/h3>\n

Training, fine-tuning, evaluation, research, generation and indexing are not a single use case.<\/p>\n<\/article>\n

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Outputs<\/h3>\n

Specify restrictions on imitation, similarity, stem extraction, voice cloning and generation of competing content.<\/p>\n<\/article>\n

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Traceability<\/h3>\n

Request information about datasets, model versions, subcontractors, retention periods and withdrawal.<\/p>\n<\/article>\n<\/div>\n

Key consideration for artificial intelligence music licensing.<\/strong> Don't confuse identification, evidence, ownership, and permission. A code, a deposit, a dated file or a detection documents a specific aspect; no single element answers all questions.<\/div>\n

Implementing an artificial intelligence music licence, step by step<\/h2>\n

1. Map out the people and assets<\/h3>\n

Start by naming the work, the recording, the exact version, and the relevant files. Add the authors, composers, performers, producers, publishers, or relevant representatives. This mapping prevents a decision made about the master from being mistakenly applied to the composition, or vice versa.<\/p>\n

2. Describe the requirement using verifiable parameters<\/h3>\n

Replace general wording with observable parameters: uses, media, territory, duration, exclusivity, volumes, permitted modifications, recipients, and termination conditions. When the project changes, create a new version of the agreement or the record instead of quietly altering the history.<\/p>\n

3. Check existing documents<\/h3>\n

Review contracts, mandates, licences, split sheets, statements, invoices, and relevant exchanges. Prior authorisation, management mandates, or exclusivity may affect your room for manoeuvre. Where important stakes are involved, a professional analysis ensures a reliable interpretation.<\/p>\n

4. Keep an accessible record<\/h3>\n

Archive the final version, attachments, date, identity of the people who validated, and the corresponding files. Use a consistent naming convention and save everything in a location separate from the place of creation.<\/p>\n

Checklist before approval<\/strong><\/p>\n