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How Artificial Intelligence is Enhancing Audio Fingerprinting Technology

Writer's picture: Ben PorterBen Porter

In a world where music can be shared instantly, protecting artists’ copyrights is a growing challenge. Traditional audio fingerprinting emerged in the early 2000s to address this need, allowing platforms to identify copyrighted material by analyzing unique audio patterns.


However, as music distribution and copyright evasion evolve, standard audio fingerprinting faces new limitations. Now, AI is stepping in to advance fingerprinting, pushing the technology’s accuracy, reach, and adaptability to new levels. In this short read, we explore how AI enhances audio fingerprinting for music copyright detection, tackling modified audio, deepfakes, and unlicensed covers to protect artists' rights.


AI Audio Fingerprinting

The Basics: What is Audio Fingerprinting?

Audio fingerprinting transforms a song or audio clip into a unique digital “fingerprint” based on characteristics like rhythm, pitch, and timbre. If you’ve ever “Shazamed” a song, you’ve already seen audio fingerprinting in action—this technology allows Shazam to identify songs within seconds. However, in serving as a reference point, audio fingerprints can also enable copyright detection systems to detect and flag unauthorized content by comparing it to database entries.


Unfortunately, however, traditional fingerprinting was only designed for exact matches of a song's master, limiting its effectiveness with more modern forms of music piracy such as modified audio, unlicensed covers, or AI-generated content that sounds close to originals. If you're looking to learn more about these infringements, check out our blog on the subject.


How AI is Expanding the Power of Audio Fingerprinting

To combat these modern infringements, the industry is equipping itself with powerful AI-powered technology, such as CoverNet, capable of recognizing even the most elusive forms of piracy. As such, AI's influence on audio fingerprinting is reshaping copyright detection by enabling new capabilities:


- Recognizing Modified Audio: AI systems detect audio with small changes, like altered pitch or tempo, which traditional fingerprinting often misses.


- Enhanced Pattern Recognition: Deep learning allows AI to identify subtle characteristics in music across genres, spotting patterns that older methods might overlook.


- Detecting AI-Generated Content: AI-driven copyright detection can recognize deepfake vocals and AI-generated music patterns that standard fingerprinting cannot, helping to protect rights holders in real time against modern piracy.


The Role of CoverNet in Modern Copyright Detection

As digital music distribution grows more complex, copyright challenges have evolved beyond traditional detection methods. CoverNet integrates AI with audio fingerprinting to address these exact issues, identifying modified audio, AI-generated music, and unlicensed covers across all key streaming and sharing platforms.


As the industry adapts to rapid AI advancements, tools like CoverNet set a new standard for copyright protection, empowering artists to safeguard their creations.


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