This AI Tool Can Generate Images From Music This Is Mind-Blowing by Jim Clyde Monge Aug, 2023 Generative AI

Striking equilibrium between AI’s efficiency and the invaluable touch of human creativity becomes pivotal. NLP (Natural Language Processing) is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP systems are used to understand and process human language, and they can be used genrative ai for a variety of tasks, such as machine translation, text summarization, and question answering. Generative AI is a type of artificial intelligence that can create new content, such as text, images, or music. It is often used in conjunction with NLP and NLG to create more natural and engaging interactions between humans and machines.

Indeed, in some cases we don’t even know if a piece of music is human-created or artificially generated. This poses existential challenges to musicians and all creators; it undermines the concept of copyright and the intellectual property (IP) framework that underpins  our industry. This presentation will discuss the impact of new AI technologies on music rights. We will discuss authorship of AI generated works, and the extent to which AI can be considered a tool. We will also cover the issue of text and data mining, and how exceptions to the reproduction right are affecting rightsholders and allowing AI systems to be trained on existing copyright works.

Rani Zarina Vaz, managing Partner & executive creative producer, Supreme Music

Yet, AI-driven music creation poses competition for creators, and raises concerns about IP protection and monetisation. While existing laws in most countries shield melodies, lyrics and chords, safeguarding thousands of AI-generated variations and preventing unauthorised usage presents challenges. It’s clear that regulation has to catch up with a framework to protect music creators.

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.

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In the last month, music executives[i], rightsholders[ii], and artists[iii] have raised concerns about the dilution of human artists’ earnings and prominence, as well as the unlicensed scraping of artists’ works for the purposes of AI training. This week, the Financial Time reported that Google and Universal Music are in early stage talks to licence artists’ music and voices to feed generative AI engines. On the one hand, it offers up a way forward that addresses at least some of the legal, financial and ethical issues around AI-generated music, and opens up new creative frontiers… but equally, it might unleash a tidal wave of sameness. It’s a seamless fusion of creativity and technology that opens up endless possibilities for content creators and professionals in need of custom soundtracks.

Therefore, it is necessary to use a case-by-case assessment to determine the author of the work and to demonstrate the presence of the level of originality and human intellectual effort required to obtain protection under copyright law. This can also be done through reverse engineering operations that manage to qualify the human interventions or contributions in the use of the AI system that led to obtaining that particular content. It is unlikely that under English and EU law a manner or style of singing is protectable by copyright whether generated through an AI synthesiser or through vocal imitation. Whilst there has been an expansion of the subject matter of copyright protection at the EU level, an overarching principle is that one must be able to identify, clearly and precisely, the protected subject matter (Cofemel). It difficult to see how a voice/style of singing could attract protection in this way.

Everyone’s at least a bit scared of Artificial Intelligence (AI).

In summary, generative AI is a broader field that includes NLP and NLG as specific areas of focus. NLP enables computers to process and understand human language, while NLG specifically focuses on generating human-like text. Both NLP and NLG are important components of generative AI, enabling systems to understand and generate text in a wide range of applications. NLG, on the other hand, is a subfield of NLP that specifically focuses on the generation of human-like text.

Yakov Livshits

• Firstly, Government must put copyright and IP protection at the heart of its approach to AI, and commit categorically to there being no new copyright exceptions.
• Knowing that AI has a part in the production has a tremendous impact on consumer sentiment toward the work.
• It utilises an algorithm called “Clara”, created by Christine Payne, and it is trained on the same dataset of J.S.

If neural synthesis represents machine learning of sound as signal, where do we find AI generation of symbolic music? With ChatGPT, OpenAI turbocharged a chatbot with a large language model (LLM) to make a formidable and highly contested prompt-based writing system. DALL-E 2 from the same company takes natural language input to a diffusion model, a probabilistic technique to generate a specific image in an information space of all images. Competitor Stability AI has open-sourced its Stable Diffusion neural network, leading to a range of code forks, including one for music. Riffusion modified the model to generate music where a prompt describing music generates an image that is the frequency spectrogram of the imagined sound.

As noted earlier, knowledge of generative AI has a strong impact on consumers’ receptivity to its various potential applications. However, at the current moment, excitement factors for AI-generated music are about 50% weaker than concern factors. Understanding what aspects of AI-generated music excite listeners are critical to increasing long-term adoption. There is growing concern among investors that this will mean market share erosion for the majors (and it probably will), but there is still a play for traditional labels and publishers, by licensing AI at the top. In doing so, they can benefit from the shift, just in the same way that major labels benefit from the rise of independent labels and artists through owning distribution platforms. That opportunity, though, requires the right approach and for it to be taken fast.

While TikTok has deals in place with labels in order to licence music (meaning artists then receive royalties from having their music used on the platform), the use of AI tracks is a much greyer area, and importantly, not a lucrative one. Provided that there is some copyright protection, under English law, the author of a computer-generated work is deemed to be the person “by whom the arrangements necessary for the creation of the work are genrative ai undertaken” (s. 9(3), CDPA). With a prompt-based AI tool, it is unclear whether the user inputting text prompts or the owner of the AI tool itself would be the author. While it’s exciting to play around with large language models like Chat GPT, don’t forget that we have no idea what the inputs for these technologies were. We don’t know what trained them or whether the creators’ consented or what biases may have been introduced unwittingly.

NLG techniques use AI algorithms to automatically generate text that is coherent, fluent, and contextually relevant. NLG finds applications in areas such as content creation, data reporting, personalized recommendations, and more. It is a similar situation to the one the music business found itself in with the rise of YouTube, where people began using hit songs as soundtracks to videos they had created. This is a book for Python programmers who are keen to create and have some fun using generative models.

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However, in June 2022, the UK IPO announced a proposal to allow TDM for any purpose at all. The proposed exception would have allowed commercial AI tools to be trained on all music without requiring a licence or compensating rightsholders, making the UK one of the most permissive places for AI research in the world. This received significant objection from the music industry, which described it as “music laundering”.

In addition to that, the government will also support rights holders by ensuring there are “protections (e.g. labelling) on generated output”. What would happen, for instance, if a human musician used AI to write music but sung it themselves? The position surrounding authorship and ownership of AI-generated music is evidently unclear.

Audeze creates products, including headphones and microphones with built-in AI-driven noise reduction systems to provide higher quality audio. Tencent Music’s streaming service reportedly hosts over 1,000 songs featuring AI-generated vocals, which have collectively received millions of plays. Musicians have also reacted to the general unease generated by ChatGPT and Bing’s AI chatbot. Bogdan Raczynski, reading transcripts of the chatbots’ viral discussions with humans, says over email that he detected “fright, confusion, regret, guardedness, backtracking, and so on” in the model’s responses. It isn’t that he thinks the chatbot has feelings, but that “the emotions it evokes in humans are very real,” he says.

He commented that AI has the ability to process huge amounts of data, drawing together knowledge from thousands of sources and making it quick and easy to access. This means artists now have more information from which they can draw inspiration, which could lead to new ideas and insights that they may not have had without this collaborative partnership. Thought-leader Bernard Marr has claimed that naysayers believe AI could lead to a decline in human creativity and innovation, but an alternative take is that it offers a wealth of new ways to improve imagination and creative thinking.