Frequently Asked Questions

CoverNet is an advanced AI-powered service designed to meticulously scan all music streaming & sharing platforms for undetected copyright usage. In addition to detecting basic unauthorized usage of an artist’s repertoire, CoverNet is also capable of flagging unlicensed covers & AI-generated vocal deep fakes – even with considerable variations in voice, language, pitch, style, or tempo. CoverNet’s intuitive dashboard allows for an easy visualization of all important metrics, providing an integrated solution for publishers, CMOs, PROs & record labels.

CoverNet tackles the critical issue of copyright infringement in the digital music landscape. Traditional detection methods often overlook unlicensed covers & are unequipped to recognize sophisticated AI-generated vocal clones. CoverNet’s spot-on music similarity detection technology fills this gap by offering real-time reporting & alerts, enabling swift & decisive actions when dealing with copyright infringements – thus safeguarding intellectual property.

While there are tools that offer some level of copyright detection, none are as comprehensive & granular as CoverNet. Designed to scan all music streaming & sharing platforms using our advanced music similarity detection technology, CoverNet offers a level of precision that is unprecedented in the music industry – whether it’s to identify unlicensed music covers or AI-generated vocal deep fakes. Thanks to CoverNet, industry professionals are now able to put a number on their missed revenue opportunities from performing rights, as well as monetize infringing content.

Our clients benefit from CoverNet’s all-encompassing feature set. Our detailed, line by line reporting structure allows them to quickly sort through flagged content and to act immediately on copyright infringements. CoverNet’s intuitive dashboard streamlines everything from tracking to action-taking, making it an ideal solution for all music stakeholders, regardless of their role in the industry.

CoverNet is a revolutionary platform that offers features that are truly unique in the realm of music copyright enforcement & market control. CoverNet doesn’t just track standard copyright infringements – it also identifies unlicensed music covers & AI-generated vocal deep fakes across all music streaming & sharing platforms. This significantly expands the scope of performing rights, thereby increasing revenue opportunities for rights holders by alerting them in real time about masters that they may not have been aware of. Additionally, CoverNet is capable of reporting all tracks that unsolicitedly utilize the vocal identity of artists, typically in the form of AI-generated vocal deep fakes. This level of insight allows for unparalleled market control, making CoverNet an invaluable tool for anyone serious about protecting their musical assets.

CoverNet provides rights holders with a comprehensive solution tailored to the modern music landscape. Not only can it swiftly identify music covers that have not secured the necessary mechanical licenses, but it also detects AI-generated vocal deep fakes. Furthermore, CoverNet ensures that performance rights are not overlooked, enabling rights holders to claim royalties from public performances of their works, even if they're in a covered or modified form. By offering this multi-faceted approach, CoverNet streamlines the claims process, ensuring that rights holders receive all the revenue they are entitled to, from mechanical to performance royalties. This holistic approach amplifies revenue streams and safeguards the interests of artists and producers in an increasingly complex digital music ecosystem.

CoverNet’s advanced AI can efficiently identify an original track, regardless of varying genres, tempos, keys & languages. The platform uses this to recognize & provide information on any repurposed covers of the original, including track modifications such as speed & pitch variations.

CoverNet currently works with multiple sources, including YouTube, Spotify, and Apple Music. Expanded compatibility is coming soon to popular streaming platforms such as TikTok, Amazon Music, Deezer, Tidal, Pandora, SoundCloud, and Vimeo.

Yes, CoverNet can recognize covers accurately & efficiently, regardless of the language.

Yes, CoverNet can analyze music files or links. The tool offers the option of sending links instead of loading music files, which helps safeguard musical assets and protect against potential copyright infringement.

CoverNet differentiates itself through its unique combination of precision & versatility. While many other tools focus solely on standard copyright infringements, CoverNet extends its reach to identify deep fakes & unlicensed covers across all platforms. It's not just a "stand-alone" service; it also adds value to tracking companies by offering licensing options for our proprietary technology. In addition to its cutting-edge AI model capable of identifying musical similarities across different genres, keys, & languages, CoverNet provides actionable insights, facilitating efficient & effective copyright claims.

MatchTune utilizes a method of collecting publicly available information from selected platforms to determine video monetization. Our analysis involves scrutinizing each video to ascertain whether ads are being run by the platform. It’s important to note that while we can detect ad presence, we do not have specific insights into whether the monetization benefits the platform, the video owner, or another party who may have claimed a portion or all of the revenue. Additionally, we incorporate information accessible from APIs and Content Management Systems provided by these platforms to enhance our analysis.

Detected means that the video's audio track was scanned and positively identified by the platform's detection algorithm (e.g., ContentID for YouTube). Conversely, not detected indicates the platform was unable to match the audio content of the video against known copyright-protected material.

Red indicates that the video is monetized, but the audio was not identified by the platform's detection algorithm (e.g., ContentID for YouTube). Green indicates that the video is not monetized, and the audio was not detected. Blue indicates that the video is not monetized, but audio has been detected and properly licensed or claimed.

Suspicious songs are typically linked to the correct rights holders if they have been detected by a platform's detection algorithm (such as "Content ID" for YouTube). CoverNet provides insights into the track's status, especially when a song is not detected.

We're currently focused on YouTube and are in the beta testing phase for integration with 8 other platforms, including Vimeo, Spotify, Apple Music, TikTok, Pandora, Tidal, and more.

MatchTune is primarily used by publishers, PROs, labels, and artists, catering to a range of needs from claiming works on licensed platforms to identifying enforcement targets.

The "Official" section includes the complete collection of videos hosted on the artist's official YouTube account, including their Vevo channel, as well as accounts on streaming platforms like Spotify and Apple Music. This serves as a central repository for all content officially released and associated with the artist on these platforms.

"Masters" are results identified through digital fingerprinting technology, which detects audio correlations across tracks. This process can reveal various adaptations of a song that incorporate original elements from the master recording, such as remixes, altered versions, and unauthorized uses of the track.

"Covers" refer to interpretations of the artist's songs recreated with different instrumentation and without using any original elements. Our proprietary AI technology identifies covers by analyzing tracks and flagging them based on their resemblance to the structure and melody of the original work. Covers typically involve new performances by other artists, often with distinct styles or arrangements, making them different from remixes or sampled versions.

The "AI" section features a list of works by various artists in which the selected artist's voice has been digitally used in place of the original singer’s. These versions are generated using deepfake technology or voice-swapping techniques, allowing the artist's voice to perform a song they didn’t originally record. This section highlights a growing trend in AI-driven audio manipulation, where technology enables new, hybrid works by altering vocals while keeping the original instrumental track or composition intact.