Metadata Matters: How Poor Music Data Leads to Undetected Infringement

Music metadata—the descriptive information about songs including titles, artists, composers, publishers, and rights holders—plays a critical but often overlooked role in copyright protection. Poor metadata quality creates conditions where infringement goes undetected, rights holders lose revenue, and legitimate licensees face unexpected liability. Understanding how metadata failures enable infringement helps explain why music copyright enforcement has become both more aggressive and more chaotic in 2025.

Metadata serves as the foundation for copyright tracking, royalty distribution, and infringement detection systems. When metadata is incorrect, incomplete, or missing, automated systems cannot match musical works to their copyright owners (https://legalblogs.wolterskluwer.com/copyright-blog/copyrights-critical-mess-music-metadata/). This breakdown has resulted in over $700 million in unmatched or unclaimed royalties—cases where the Music Licensing Collective could not match metadata with artists to pay out royalties (https://scrapingrobot.com/blog/music-royalties/).

Content identification systems rely on accurate metadata to function. YouTube's Content ID, Audible Magic, and similar technologies use audio fingerprinting combined with metadata to identify copyrighted music in user uploads (https://www.waterandmusic.com/music-ai-content-copyright-detection-deepfakes/). When metadata is poor, these systems fail to recognize infringement or incorrectly flag legitimate content. Both outcomes harm rights holders—undetected infringement means lost revenue while false positives damage relationships with legitimate licensees.

Music Licensing, Inc. operates AI-powered platforms enabling algorithms to learn from music metadata including genre, mood, tempo, key, and lyrics (https://news.allnim.com/ai-nim-and-music-metadata-0f0261d33db5). These trained AI models generate new songs incorporating characteristics from original songs' metadata. The platform records metadata used in AI training processes, creating trails of metadata provenance to identify potential copyright infringement. Automated systems monitor metadata usage and detect potential infringement instances, aiming to protect copyright owners' rights.

However, these detection systems only work when input metadata is accurate. Poor metadata creates blind spots where infringement occurs but remains invisible to tracking technologies. Neural fingerprinting technologies detect both AI-generated tracks and copyright infringement (https://www.forbes.com/sites/virginieberger/2025/10/10/when-machines-police-machines-how-neural-fingerprinting-detects-ai-music-), but they require reference databases with comprehensive, accurate metadata to function effectively.

Metadata deficiencies lead to recommender system biases and lack of adequate information for general public, cultural archives, and historical research. Recommender systems play crucial roles in online dissemination of copyrighted works, suggesting specific information to service recipients or prioritizing information. Missing or incorrect metadata in datasets undermines these systems' effectiveness and accuracy.

Interoperability challenges compound metadata problems. Overcoming missing or incorrect metadata requires interoperability between datasets. If models can consistently describe metadata, they enable linking entities and concepts from various datasets. However, numerous challenges exist in music metadata design related to interoperability (https://legalblogs.wolterskluwer.com/copyright-blog/copyrights-critical-mess-music-metadata/). Interoperability requires designing music metadata models across different genres and historical periods accommodating various use cases over heterogeneous data sources.

Discrepancies in metadata granularity and provenance between datasets stem partly from disparate music industry release processes. Smaller independent labels and artists commonly release music online through distributors. Research commissioned by the UK Intellectual Property Office noted that when smaller labels or artists move work between distributors, new distributors often freely decide to assign new ISRCs instead of adopting predecessors' codes (https://legalblogs.wolterskluwer.com/copyright-blog/copyrights-critical-mess-music-metadata/).

This ISRC reassignment creates tracking failures. The International Standard Recording Code system operates in decentralized fashion with each national authority managing ISRCs in specific territories. Public ISRC search tools on IFPI's website show incompleteness for lesser-known independent repertoires and additional metadata generally, likely caused partially by no requirement to submit metadata during or after code assignment processes.

Economic challenges hinder metadata improvements. Music metadata is created and held by many different actors including collecting societies, music labels, publishers, distributors, online content-sharing service providers, and artists themselves. This creates a fragmented landscape full of data silos (https://legalblogs.wolterskluwer.com/copyright-blog/copyrights-critical-mess-music-metadata/). Interoperability between silos is hampered not just by metadata design issues but mostly by shortage of incentives to share for influential actors. Having wealth of music metadata gives platforms, labels, and collecting societies advantages versus competitors.

Poor metadata enables undetected infringement in multiple ways. First, content identification systems fail to recognize copyrighted material lacking accurate identifying information. Second, rights holders cannot send takedown notices for infringement they cannot detect. Third, legitimate licensees might unknowingly infringe when searching for rights holders using poor metadata fails to identify all necessary parties for clearance. Fourth, royalty collection societies cannot distribute payments to rights holders they cannot identify from metadata, reducing financial incentives for creating and distributing music.

AI-generated content complicates metadata challenges. Generative AI trains on copyrighted music without permission. Neural fingerprinting detects both AI-generated tracks and copyright infringement, but these systems require robust metadata to determine whether AI tracks are derivative of copyrighted works (https://www.forbes.com/sites/virginieberger/2025/10/10/when-machines-police-machines-how-neural-fingerprinting-detects-ai-music-). Derivative detection assesses whether AI tracks are based on copyrighted material, pertaining to infringement questions. Poor metadata makes this assessment unreliable or impossible.

Platforms like CoverNet by MatchTune use AI copyright and infringement detection going beyond audio fingerprinting (https://www.matchtune.com/covernet-copyright-infringement-detection). They detect unlicensed use, AI vocal clones, derivative works, and modified audio including slowed-down, sped-up, pitch-shifted, or filtered versions. However, effectiveness depends on reference databases with complete, accurate metadata. Gaps in metadata create detection blind spots allowing infringement to proliferate undetected.

Digital Service Providers increasingly enforce metadata quality standards. Apple Music and Spotify have become more vigilant about metadata quality, meaning music could get removed if inadequate information exists (https://diymusician.cdbaby.com/releasing-music/why-you-cant-afford-to-get-your-music-metadata-wrong/). Specifying whether songs are original compositions or cover songs ensures no copyright infringement. This enforcement pressure creates compliance burdens but improves overall metadata ecosystem health.

The fundamental problem remains economic. Most influential metadata holders lack incentives to share information that provides competitive advantages. Until regulatory intervention, industry standards, or marketplace pressures change these incentives, metadata quality will continue undermining copyright protection. Poor music data will keep leading to undetected infringement, lost revenue for creators, and licensing chaos for legitimate users throughout 2025 and beyond.