'Signal to Noise Loops v4', the New York Electroacoustic Festival, New York

Stephen Roddy

'Signal to Noise Loops v4', the New York Electroacoustic Festival, New York

Stephen Roddy

School of English and Digital Humanities

Research output: Non-textual form › Performance

Abstract

New York Electroacoustic Festival: Signal to Noise Loops v4

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 (Ireland)
Publisher	NYCEMF: New York Electroacoustic Festival
Publication status	Published - Jan 2021

UN SDGs

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

SDG 11 Sustainable Cities and Communities

UCC Futures

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

Keywords

Data
Smart Cities
Sonification
Music
sound & music computing
experimental music
Sound Art
Sensor Networks
Internet of Things (IoT)
Internet of Sounds (IoS)
Machine Learning
Artificial Intelligence
Wavetable Synthesis
Granular Synthesis

Cite this

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title = "'Signal to Noise Loops v4', the New York Electroacoustic Festival, New York",

abstract = "New York Electroacoustic Festival: Signal to Noise Loops v4 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 = "Data, Smart Cities, Sonification, Music, sound \& music computing, experimental music, Sound Art, Sensor Networks, Internet of Things (IoT), Internet of Sounds (IoS), Machine Learning, Artificial Intelligence, Wavetable Synthesis, Granular Synthesis",

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year = "2021",

month = jan,

language = "English (Ireland)",

publisher = "NYCEMF: New York Electroacoustic Festival",

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N2 - New York Electroacoustic Festival: Signal to Noise Loops v4 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 - New York Electroacoustic Festival: Signal to Noise Loops v4 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 - Data

KW - Smart Cities

KW - Sonification

KW - Music

KW - sound & music computing

KW - experimental music

KW - Sound Art

KW - Sensor Networks

KW - Internet of Things (IoT)

KW - Internet of Sounds (IoS)

KW - Machine Learning

KW - Artificial Intelligence

KW - Wavetable Synthesis

KW - Granular Synthesis

UR - https://nycemf.org/submissions/

M3 - Performance

PB - NYCEMF: New York Electroacoustic Festival

ER -

'Signal to Noise Loops v4', the New York Electroacoustic Festival, New York

Abstract

UN SDGs

UCC Futures

Keywords

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Cite this