Skip to main navigation Skip to search Skip to main content

'Signal to Noise Loops v4' for S3 of the Video Sound Archive

Research output: Non-textual formPerformance

Abstract

Signal to Noise Loops v4 for S3 of the Video Sound Archive, February 2022.

Signal to Noise Loops v4 is a data-driven audiovisual piece. It is informed by principles from the fields of IoT, Sonification, Generative Music, and Cybernetics. The piece maps data from noise sensors placed around Dublin City to control a generative algorithm that creates the music. Data is mapped to control the sound synthesis algorithms that define the timbre of individual musical voices and data is also mapped to control post-processing effects applied to in the piece.
The first movement consists of data recorded from noise level sensors around Dublin in March 2019. This is before the COVID-19 pandemic and the bustling nature of the city is well represented. The second movement consists of data recorded in March 2020 when restrictive and social distancing measures were introduced culminating in a full lockdown on March 27th. This section is notably more sedate.
The piece was created with Python, Ableton Live, and Processing.
Original languageEnglish (Ireland)
PublisherVideo Sound Archive
Publication statusPublished - 2022

UCC Futures

  • Future Humanities Institute
  • Future of Networks, Systems & Cybersecurity 
  • Artificial Intelligence and Data Analytics

Keywords

  • Sound
  • Music
  • Cybernetics
  • Sonification
  • sound & music computing
  • Machine Learning
  • Artificial Intelligence (AI)
  • Sensor Networks
  • Media Engineering
  • Digital Art
  • Generative Music
  • Wavetable Synthesis
  • Granular Synthesis

Fingerprint

Dive into the research topics of ''Signal to Noise Loops v4' for S3 of the Video Sound Archive'. Together they form a unique fingerprint.

Cite this