Creating Signal to Noise Loops v4

Stephen Roddy

doi:http://hdl.handle.net/1853/67391

Creating Signal to Noise Loops v4

Stephen Roddy

School of English and Digital Humanities

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

Program notes:
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 language	English
Title of host publication	Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022)
DOIs	https://doi.org/http://hdl.handle.net/1853/67391
Publication status	Published - 27 Jun 2022
Event	27th International Conference on Auditory Display (ICAD2022) - Online Duration: 24 Jun 2022 → 27 Jun 2022 https://icad2022.icad.org/

Conference

Conference	27th International Conference on Auditory Display (ICAD2022)
Period	24/06/22 → 27/06/22
Internet address	https://icad2022.icad.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

UCC Futures

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

Keywords

Music
Data
sound & music computing
Sonification
Network Communications
Computer Networks
Artificial Intelligence (AI)
machine learning (ML)
Smart Cities
Internet of things (IoT)
Internet of Sounds (IoS)
Media Engineering
Sensor Networks
Generative Music

Access to Document

http://hdl.handle.net/1853/67391

Cite this

@inproceedings{9eaf68eb40b64ad19ca922296bfe6281,

title = "Creating Signal to Noise Loops v4",

abstract = "Program notes: 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.",

keywords = "Music, Data, sound \& music computing, Sonification, Network Communications, Computer Networks, Artificial Intelligence (AI), machine learning (ML), Smart Cities, Internet of things (IoT), Internet of Sounds (IoS), Media Engineering, Sensor Networks, Generative Music",

author = "Stephen Roddy",

year = "2022",

month = jun,

day = "27",

doi = "http://hdl.handle.net/1853/67391",

language = "English",

booktitle = "Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022)",

note = "27th International Conference on Auditory Display (ICAD2022) ; Conference date: 24-06-2022 Through 27-06-2022",

url = "https://icad2022.icad.org/",

}

TY - GEN

T1 - Creating Signal to Noise Loops v4

AU - Roddy, Stephen

PY - 2022/6/27

Y1 - 2022/6/27

N2 - Program notes: 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.

AB - Program notes: 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.

KW - Music

KW - Data

KW - sound & music computing

KW - Sonification

KW - Network Communications

KW - Computer Networks

KW - Artificial Intelligence (AI)

KW - machine learning (ML)

KW - Smart Cities

KW - Internet of things (IoT)

KW - Internet of Sounds (IoS)

KW - Media Engineering

KW - Sensor Networks

KW - Generative Music

UR - https://icad2022.icad.org/wp-content/uploads/2022/06/8-Stephen-Roddy-ICAD2022_11.pdf

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DO - http://hdl.handle.net/1853/67391

M3 - Conference proceeding

BT - Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022)

T2 - 27th International Conference on Auditory Display (ICAD2022)

Y2 - 24 June 2022 through 27 June 2022

ER -

Creating Signal to Noise Loops v4

Abstract

Conference

UN SDGs

UCC Futures

Keywords

Access to Document

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Cite this