Introduction
Over the past few years, technology has evolved significantly, witnessing the emergence of new tools harnessing AI to increase productivity and simplification. Few domains have been spared from this influence, and the music industry has undergone its fair share of changes. Most notably, in a generative capacity, the arrival of AI-generated “deep fakes” have flooded platforms such as YouTube, TikTok, and many more.
Deep fake music divides opinion; some see it as a further opportunity to streamline the music-making process, while some see it as emblematic of an inevitable robot takeover. Regardless of where you stand, deep fake music is undoubtedly revolutionary for music, so how does it exist currently in the music production process? In this article, we will dive into the world of AI-generated Deep Fake Music, exploring the intricate challenges it creates for the industry in technology, ethics, and the economy.
Creating Deep Fake Music
Deep fakes rely on advanced techniques of machine learning and audio signal processing, and to achieve nearly undetectable deep fake reproductions, there are several stages. The process begins with the collection of audio data from a reference artist, encompassing song recordings, interviews, and live performances. This diverse data serves as a foundation to reproduce the nuances of the voice, and encapsulate the musical style of the artist.
Once this data is collected, it undergoes "preprocessing", aimed at extracting finer features such as frequency, tonality, and rhythm. A deep learning phase for the model follows, often based on what are known as “recurrent neural networks” (RNN) or “long short-term memory neural networks” (LSTM); networks which enable models to learn complex patterns from sequences of data such as videos or audio recordings. This model is trained to recognize complex patterns and distinctive features of the artist's voice and musical style. After all of this, a final refinement phase adjusts certain parameters to improve the output quality and make the deep fake as realistic as possible.
With all of these steps completed, the model now has the ability to generate musical sequences that, in the majority of cases, shock us with their similarity to the artist's style, both in terms of vocals and rhythm. With models such as these, artificial intelligence can easily create musical reproductions of any current song with the voices of any artist; for example, John Lennon covering a song by Taylor Swift. The model can even create a new track with the voices of existing artists, such as in April 2023 when a song titled "Heart On My Sleeve" was released, a piece generated by AI and featuring the voices of The Weeknd and Drake.
Controversial Usage of Deep Fake Music:
The ability to recreate an artist's voice without their consent raises significant ethical concerns regarding artistic integrity. With this technology, artists could be misrepresented, and their works could be used for controversial purposes which harm their public image or reduce their popularity. With generative AI technology becoming more available, it’s almost guaranteed that a select few will harness it in destructive ways.
Added to this is a real risk for the economic model of the music industry. Currently, despite systems in place to filter content on streaming platforms or user-generated content (UGC), it is incredibly difficult to detect deep fake music. If these deep fakes were to spread further without an effective means of detection, it would open the door to unlimited distribution of AI-generated copyright infringements which do not pay royalties to the original creators. This, a blatant violation of the fundamental principle of fair remuneration for artistic work, could leave artists with significant financial losses.
At MatchTune, we wanted to provide online platforms with the toolset they need to detect AI-generated deep fakes, safeguard their rights and foster fair royalty management. This led to the creation of CoverNet, an AI-powered music copyright platform, capable of detecting every single deep fake song, along with unlicensed covers, modified audio and one-to-one master usage, across on a wide range of platforms. By enabling artists to have a more detailed oversight and a broader perspective on their content, our aim is to contribute to foster an equitable online music space which distributes revenue fairly to rights holders.
Conclusion:
The emergence of musical deep fakes marks a new era in the music industry, in which artificial intelligence acts dichotomically as a creative force, but also a major ethical and economic challenge. While these technological advancements showcase the innovative potential of artificial intelligence, allowing for musical reproductions with remarkable precision, they also raise alarms around artistic integrity and ethics.
Looking towards the future, it is encouraging to note that innovative solutions are emerging to address these complex challenges. These solutions aim to usher in a new era where artists' rights are preserved, and fair compensation is ensured in a continually evolving musical landscape. To this end, CoverNet excels, standing as the music industry’s sole solution to copyright management, capable of addressing all facets of contemporary rights violations. You can learn more about the AI-powered platform here.
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