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A Bottleneck of Opportunity

 
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A Bottleneck of Opportunity

Floris Demandt

      Title
      • A Bottleneck of Opportunity
      Subtitle
      • Designing an interface for the experimental exploration of RAVE models
      Author
      Date
      • 21.06.2024
      Description
      • The vast improvements of generative audio AIs have created powerful new tools to generate audio
        content for a wide target group. Popular audio generation models, like Suno and Udio, enable users to
        generate high-quality music by writing a text prompt. This makes them very accessible and easy to use
        for the average person, but for audio professionals such as musicians and sound designers, they lack
        expressivity and adjustability because of their text prompt interface and non-real-time audio
        generation. Novel approaches to AI audio generation like IRCAM RAVE offer solutions to these
        problems by generating audio in real time and abandoning the need for text prompting and offering
        direct and precise control of the generation through the model’s latent variables. This inevitably raises
        the question of how an interface for a real-time-generative-audio-model that abandons the need for
        text prompting could be designed and if this method offers potential for new sound design and music
        tools.
        This thesis explores one of various ways of designing an interface for a real-time-generative-audio-
        model by giving the user methods to experimentally explore RAVE models with a variety of
        interaction methods. These interaction methods range from simple sliders to 3D physical models, with
        each method exploring a distinct way to interact and therefore generate audio content. During an
        expert review, the interface was evaluated to gather feedback on its perception and usability, as well as
        the interaction methods used. Using this expert review, the interface was enhanced with new
        interaction methods. This thesis concludes with an outlook for future generative audio models and
        what audio professionals may expect to incorporate them into their workflows.
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