Copyright Infringement Detection: Some Background
In a digital age where music can be effortlessly shared and distributed, protecting the rights of artists has become an increasingly complex & elusive challenge. Music copyright infringement detection is a critical aspect of this protection and, as a result, audio fingerprinting emerged in the early 21st century as a promising solution. However, while it offers many advantages, audio fingerprinting has its limitations. In this blog, we will explore the concept of audio fingerprinting in music copyright infringement detection and delve into the reasons why it is not a complete solution.
What is Audio Fingerprinting and What Makes It Great?
Just like a physical fingerprint, an ‘audio fingerprint’ is a one-to-one condensed & characterized profile of a song, which allows copyrighted material to be identified and verified. To create an audio fingerprint, an audio file is analyzed for features such as spectral content, timbre, and rhythm. A unique digital fingerprint is created from these features; a condensed digital representation of the audio which can be compared against a vast database of references to identify a match.
When a perfect match is found, the system can trigger copyright detection or take other predefined actions. An example of fingerprinting in action is YouTube’s ‘Content ID’, which can recognize the use of copyrighted music on the platform, flagging the content it appears in and offering remuneration to the true rights-holders of that song.
The Limitations of Audio Fingerprinting
Audio fingerprinting has gained popularity as a music copyright detection tool for its speed, efficiency and accuracy, but its 1-to-1 nature makes it largely redundant in identifying more obscure copyright infringements such as unlicensed covers; of which there are plenty on platforms such as YouTube.
Furthermore, as the online world moves into another tier of possibilities, catalyzed by the growth of AI-generated music, the need has arisen for more detection which fingerprinting simply cannot manage. When it comes to "deep fake" music, traditional fingerprinting methods are rendered somewhat unequipped. These AI algorithms can generate music that is eerily realistic and often indistinguishable from human compositions, making it incredibly difficult for standard audio fingerprinting to detect the subtle nuances that distinguish real from fake. AI-generated vocal clones can blend seamlessly with authentic tracks, making it almost impossible to accurately identify and attribute authorship without a tool such as CoverNet by MatchTune. If you want to learn a little more about the legal & attribution complications surrounding AI-generated music, check out this blog.
So, while audio fingerprinting has undoubtedly improved the efficiency and accuracy of music copyright detection, it is not a one-size-fits-all solution, and copyright enforcement agencies, music platforms, publishers, and even artists themselves must understand its strengths and weaknesses. As technology continues to evolve, so too must our strategies for safeguarding the rights of those who enrich our lives with music.
In order to identify the vast majority of modern infringements online, more all-encompassing tools must be harnessed. This is why CoverNet was launched; a holistic approach to modern copyright infringement detection. CoverNet meticulously scans all DSPs and UGC platforms for infringements, displaying these in an intuitive, customizable interface. The platform can recognize every single form of copyright infringement, from unlicensed covers to AI-generated vocal clones.
Discover CoverNet: https://www.covernet.ai/
Learn more about MatchTune: https://www.matchtune.com/
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