How ACRCloud helped streamline Tunearo’s quality control

Music distribution quality control has to balance two important goals. Artists need releases reviewed quickly, but distributors must also take reasonable steps to ensure submitted recordings can be delivered lawfully. Treating either goal as an afterthought can cause delays, takedowns and avoidable disputes later.

Manual review remains central to Tunearo’s process. Our team checks artwork, metadata, contributor information, audio quality and the evidence supplied by the customer. The challenge is that ownership cannot always be established from a filename, title or artist name. The same recording may be renamed, submitted with incomplete metadata or uploaded by somebody who is not the rights holder.

Adding ACRCloud to the workflow gave our reviewers another useful layer of information: the sound of the recording itself.

What ACRCloud does

ACRCloud provides automatic content-recognition technology. Its music-recognition service uses audio fingerprinting to compare audio with a large reference database and return possible matches together with available identifiers and metadata.

An audio fingerprint is not the audio file and it does not depend only on written metadata. It is a compact representation of the recording’s acoustic characteristics. That makes it useful when two files sound alike but arrive with different titles, artist names or filenames.

ACRCloud describes its recognition catalogue as containing more than 150 million tracks with ongoing updates. The company also offers copyright-compliance and data-deduplication tools for distributors, labels and digital platforms.

Derivative Works Detection finds more than exact copies

Tunearo uses ACRCloud’s Derivative Works Detection to help identify copyrighted recordings that may have been altered before submission. A basic exact-match check can miss audio that has been sped up, slowed down or pitch-shifted. Derivative Works Detection is designed to surface possible matches even when those kinds of modifications are present.

This matters because changing the speed or pitch of a recording does not automatically create new ownership rights. By finding possible derivative or modified versions during QC, we can investigate the source recording and request permission or licensing evidence before the release is delivered.

AI Music Detection adds another QC signal

Tunearo also uses ACRCloud’s AI Music Detection to assess whether submitted audio is more likely to be AI-generated or human-created. The service can return a prediction, an AI-probability score and supporting source information for recognised generative-music systems.

We use that result as a review signal—not as definitive proof of how a track was created. A probabilistic result may prompt our team to check the release more carefully, confirm that metadata and declarations are accurate, and consider any applicable store requirements. It does not replace a human decision or determine copyright ownership by itself.

How recognition fits into Tunearo QC

When a release enters review, audio recognition can provide an early indication that a submitted recording may correspond with an existing work. The result becomes part of the evidence available to our quality-control team; it does not make the final decision.

1. The artist submits a release. Tunearo receives the audio, artwork, metadata and contributor details.
2. The audio is checked. Derivative Works Detection can surface modified catalogue matches, while AI Music Detection provides an additional signal about likely AI-generated audio.
3. A reviewer assesses the result. The team considers match confidence, metadata, release context and information supplied by the customer.
4. We ask for evidence when necessary. A customer may need to provide a licence, agreement or another explanation of their rights.
5. The release proceeds or is returned. Releases that satisfy the checks can continue toward delivery; unresolved concerns are held back.

Why this has made reviews faster

Faster QC does not come from skipping checks. It comes from giving reviewers better signals earlier. Before audio recognition, a possible conflict could require more manual searching and comparison. A recognition result can narrow that investigation and help the reviewer decide what question needs to be answered.

• Possible catalogue matches can be surfaced earlier in the review process
• Reviewers receive additional evidence before making a rights decision
• Straightforward submissions can move through QC more efficiently
• Higher-risk releases can receive focused manual attention
• Artists can be asked for licences or ownership evidence before delivery

This allows the team to spend less time on repetitive discovery and more time interpreting results, reviewing evidence and communicating clearly with artists. It also means low-risk submissions are less likely to wait behind releases that require a deeper investigation.

How it helps reduce infringement risk

Rights problems are easier to address before a release is delivered. Once content reaches stores, a dispute can affect several services, create takedown work and delay legitimate royalty reporting. Identifying a possible match during QC gives everyone a chance to resolve the question at the earliest practical point.

Since integrating ACRCloud into our process, Tunearo has seen a significant reduction in potentially infringing material progressing through QC. Recognition is especially useful when metadata is misleading or incomplete because the comparison is based on the submitted audio rather than the uploader’s description alone.

Technology supports human judgement

Automated recognition is powerful, but rights ownership and content origin can be complicated. A derivative-work match does not, by itself, establish who owns the master or whether a valid licence exists. In the same way, an AI probability is not definitive proof of how a recording was created. That is why Tunearo does not treat either result as an automatic rejection.

Human review remains necessary for legitimate remasters, authorised re-releases, licensed content, public-domain material, cover songs and other cases where similarity has a lawful explanation. The best outcome comes from combining reliable technical signals with clear policies, experienced reviewers and evidence from the customer.

What artists can do to avoid delays

• Upload only recordings you own or are authorised to distribute.
• Keep licences, contracts and beat agreements available.
• Use accurate artist names, titles and contributor roles.
• Explain legitimate re-releases or catalogue transfers clearly.
• Respond to QC questions with complete, readable evidence.
• Allow enough time before the intended release date for review.

A recognition match does not need to become a long delay. When the rights are legitimate and the supporting information is organised, the review is usually much easier to resolve.

Building a safer distribution pipeline

Tunearo’s goal is not simply to move files from an upload form to a store. A responsible distributor must protect legitimate artists, maintain trustworthy relationships with platforms and reduce the chance that somebody else’s recording is delivered without permission.

ACRCloud has helped us make that process more efficient. Earlier recognition, better-focused review and clearer requests for evidence have improved turnaround times while strengthening the checks behind each delivery. As our catalogue grows, that combination of technology and human judgement will remain an important part of how Tunearo scales quality control responsibly.

Frequently asked questions

Further reading

• ACRCloud music recognition
• ACRCloud copyright compliance and data deduplication
• ACRCloud AI Music Detection
• Why releases fail music-distribution quality control

Recognition results are one part of Tunearo’s quality-control process and do not independently determine copyright ownership or legal rights.