Santiago Renteria Aguilar

Santiago Renteria Aguilar

Mr, Doctor of Philosophy Student

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Personal profile

Biography

Santiago Renteria is a transdisciplinary researcher working at the intersection of artificial intelligence, music and biology. As part of his masters he developed a “Shazam” for birdsong based on siamese neural networks, a few-shot machine learning technique capable of recognizing birds’ complex melodic sequences. Beyond machine learning one of his main interests is to develop and understand non-human forms of intelligence through artistic experimentation. In his practice, he plays with different media such as generative deep learning, spatial audio and machine listening. He has collaborated with internationally renowned artists and organizations working at the interface of art, technology, and science. Santiago's work as a creative developer has been showcased at Laboratorio de Arte Alameda, Centro Cultural Universitario Tlatelolco, Carnaval de Bahidorá and Tecnológico de Monterrey.

Research

Santiago's PhD research is concerned with how Machine Listening, understood as the automation and technical manipulation of human listening has shaped the (re)production and reception of sonic environments in arts and sciences. Modern listening machines do not obey objective relevance maxims of selectivity nor follow human-readable procedures. Their behaviour is influenced and made possible by a range of sociotechnical phenomena including but not restricted to: deep learning technologies, pervasive computing, economic forces and taste.

The first part of the project is about the development of Sonic Reanimation, a practice-led endeavour exploring the technoscientific field of species de-extinction through the artistic and scientific use of wildlife sound archives in Australia. By using deep generative techniques, Santiago seeks to bring back from the archives speculative sounds of forgotten animals while scrutinizing generative audio technologies. The reconstruction of species in different media and sensory modalities problematizes the fidelity and identity of what is actually being reanimated as this practice taps into reflective nostalgia and conditions the emergence of ecological imaginaries.

The second part of the project is focused on the interplay between listening and silencing, paying special attention to those instances enabled by machine learning in biodiversity monitoring and ecological auscultation. Here, listening is studied as an epistemic mode of expectation that informs aesthetic and technical choices with implications for how both human and non-human spaces are represented, designed, administered, policed, beautified, and maintained. More concretely, this part of the project will involve the development of computational systems for the study of Australian Magpies (Cracticus tibicen). This will be carried out in collaboration with the behavioural ecology group led by Associate Professor Amanda Ridley.

Overall, Santiago's research contributes to the transdisciplinary understanding of the sonosphere, a sonic continuum resulting from a multiplicity of listening and sound-producing agents. Through a series artistic (technical) experiments he will explore how the human-constructed boundaries between the living and the non-living, the natural and the artificial are destabilized by the automation of listening.

Funding overview

Santiago is a recipient of the full PhD studentship, awarded under the Australian Research Council Discovery Project scheme to the project ‘A Cultural and Intellectual History of Automated Labour’. Chief Investigators: Sarah Collins, Ionat Zurr, Oron Catts, and Elizabeth Stephens

Education/Academic qualification

Computer Science, Masters, Instituto Tecnologico de Estudios Superiores de Monterrey

Award Date: 1 Aug 2020

Music & Audio Engineering, Bachelors of Science, Instituto Tecnologico de Estudios Superiores de Monterrey

Award Date: 1 Aug 2017

Industry keywords

  • Creative Arts
  • Environmental

Research expertise keywords

  • Artificial Intelligence
  • Sound Studies
  • Environmental Humanities
  • Computational Audition
  • Machine Learning
  • Computer Music

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