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- Tatar, Kivanc, 1988, et al.
(författare)
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A Shift in Artistic Practices through Artificial Intelligence
- 2024
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Ingår i: Leonardo. - : MIT Press. - 0024-094X .- 1530-9282. ; 57:3, s. 293-297
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Tidskriftsartikel (refereegranskat)abstract
- The explosion of content generated by artificial intelligence (AI) models has initiated a cultural shift in arts, music, and media, whereby roles are changing, values are shifting, and conventions are challenged. The vast, readily available dataset of the Internet has created an environment for AI models to be trained on any content on the Web. With AI models shared openly and used by many globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring to music, arts, and new media?.
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| 2. |
- Cotton, Kelsey, 1994, et al.
(författare)
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Singing for the Missing: Bringing the Body Back to AI Voice and Speech Technologies
- 2024
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Ingår i: 9th International Conference on Movement and Computing. - 9798400709944 - 9798400709944
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Konferensbidrag (refereegranskat)abstract
- Technological advancements in deep learning for speech and voice have contributed to a recent expansion in applications for voice cloning, synthesis and generation. Invisibilised stakeholders in this expansion are numerous absent bodies, whose voices and voice data have been integral to the development and refinement of these speech technologies. This position paper probes current working practices for voice and speech in machine learning and AI, in which the bodies of voices are “invisibilised". We examine the facts and concerns about the voice-Body in applications of AI-voice technology. We do this through probing the wider connections between voice data and Schaefferian listening; speculating on the consequences of missing Bodies in AI-Voice; and by examining how vocalists and artists working with synthetic Bodies and AI-voices are ‘bringing the Body back’ in their own practices. We contribute with a series of considerations for how practitioners and researchers may help to ‘bring the Body back’ into AI-voice technologies.
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| 3. |
- Tatar, Kivanc, 1988, et al.
(författare)
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Sound Design Strategies for Latent Audio Space Explorations using Deep Learning Architectures
- 2023
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Ingår i: Proceedings of the Sound and Music Computing Conferences. - 2518-3672. ; 2023-June, s. 239-246
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Konferensbidrag (refereegranskat)abstract
- The research in Deep Learning applications in sound and music computing have gathered an interest in the recent years; however, there is still a missing link between these new technologies and on how they can be incorporated into real-world artistic practices. In this work, we explore a well-known Deep Learning architecture called Variational Autoencoders (VAEs). These architectures have been used in many areas for generating latent spaces where data points are organized so that similar data points locate closer to each other. Previously, VAEs have been used for generating latent timbre spaces or latent spaces of symbolic music excepts. Applying VAE to audio features of timbre requires a vocoder to transform the timbre generated by the network to an audio signal, which is computationally expensive. In this work, we apply VAEs to raw audio data directly while bypassing audio feature extraction. This approach allows the practitioners to use any audio recording while giving flexibility and control over the aesthetics through dataset curation. The lower computation time in audio signal generation allows the raw audio approach to be incorporated into real-time applications. In this work, we propose three strategies to explore latent spaces of audio and timbre for sound design applications. By doing so, our aim is to initiate a conversation on artistic approaches and strategies to utilize latent audio spaces in sound and music practices.
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