How to Distribute AI-Generated Music to Major Streaming Platforms

#Distributor Policies — Verified Categories (2026-05)

We're not affiliated with any distributor. Policies change often — sometimes monthly — so we link each distributor's official page below and date everything to May 2026. The categories here frame the landscape; the per-distributor table that follows gives specifics. Always confirm the current rule on the exact distributor you plan to use:

#AI Policy by Distributor (2026)

The most-used distributors fall into three camps — allow with disclosure, allow with limits, or reject outright. Here is where each stood as of May 2026. Tap a name to confirm the current rule on its official page:

"Accepts" / "Conditional" describes the written policy only — it does not guarantee a track clears automated detection. Linked pages are each distributor's official help/policy docs; confirm the current rule before you release.

#Industry-Wide Disclosure Standards (2026)

Two structural shifts in 2026 directly affect how AI releases are handled:

• DDEX AI Disclosure Standard — Adopted by major platforms and distributors. AI usage is reported at four nuanced levels: vocals, instrumentation, post-production, and lyrics — not a binary "AI or not AI" flag. Disclosure flows from distributor to platform.
• Apple Music Transparency Tags (March 2026) — A separate tagging system requiring disclosure across four areas: sound recording, composition (lyrics/melody), visual artwork, and music video. Delivered via distributor metadata.

Practical consequence: even on platforms that allow AI music, undisclosed AI usage can be flagged retroactively and result in removal — disclosure and acceptance are separate gates.

#Streaming Platform Policies — Common Patterns

Streaming platforms make their own decisions independent of distributors. A track can pass distributor intake and still be removed by a platform. We're not affiliated with any platform — the patterns below summarise what we observe across the industry as of May 2026. Always confirm current rules on the exact platform you ship to.

• Major streaming platforms — Generally do not categorically ban AI music, but require nuanced AI disclosure (via DDEX-compliant metadata or platform-specific tags). Unauthorised voice clones of identifiable artists are typically removed on sight. Spam filters down-rank mass-uploaded or duplicated AI tracks.
• Curated / lossless-focused platforms — Often apply similar human-impersonation rules and require disclosure tags across multiple areas (recording, composition, artwork, video). Quality review may reject tracks with obvious AI artefacts.
• Video-driven platforms — Tend to be more permissive on AI music. Content-ID-style systems may still flag tracks that closely resemble training data; some downstream services (e.g. Content ID monetisation) may be excluded for AI uploads.
• Short-form social platforms — Often allow AI music, but monetisation policies around AI content are still evolving and may not pass through every distributor.

#The Two Gates: Disclosure and Detection

There are now effectively two separate hurdles when distributing AI music:

1. Disclosure gate — Human-reviewed. You declare that your track is AI-generated. The distributor or platform decides whether to accept it based on their policy.
2. Detection gate — Automated. The platform or distributor runs your audio through an AI classifier. If the confidence score exceeds a threshold, the track may be flagged, reviewed, or rejected — even if you disclosed correctly.

The detection gate is increasingly common and operates independently of disclosure. This means a track you disclosed as AI-generated can still be rejected or removed by automated detection — and conversely, a track with a low detection score may pass even without explicit disclosure (though non-disclosure violates terms of service where required).

#How AI Audio Detection Works

AI music detectors analyse the audio file itself — not metadata or tags. They look for spectral patterns that are statistically characteristic of generative models:

• Specific frequency distributions in the vocal-and-instrument mid-band
• Phase relationships between channels that differ from recorded audio
• Micro-timing uniformity that acoustic instruments wouldn't produce
• Artefacts in the 17–20 kHz range from the model's training codec

Standard mastering (loudness normalisation, EQ, limiting) has almost no effect on these patterns. A dedicated anti-detection processing pipeline is required.

#Preparing Your Track for Distribution

1. Master for loudness — A −14 LUFS / −1 dBTP target is a safe default for most major streaming platforms (confirm on each platform). Use a genre-appropriate preset.
2. Apply Anti-AI processing — Reduces AI detector confidence scores by disrupting characteristic spectral fingerprints.
3. Scan before submitting — Use Anti-AI Master's built-in scanner to see your detection score before and after processing. Only download if the score drop is satisfactory.
4. Export as WAV 24-bit — Most distributors prefer lossless delivery.
5. Complete metadata carefully — Set correct genre, mood, and AI disclosure flags per your distributor's requirements.
6. Monitor post-release — Tracks can be removed weeks after release if a platform updates its detection models. Check regularly.

#Frequently Asked Questions