The noise floor is commonly considered to be the sum of all “unwanted” signals in an audio recording. That may be the sound of an air conditioner, a bird's song, a police siren or a human voice—depending on where you're listening from.

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A recording's noise floor can be influenced by the material spatial properties of a room or an environment, but most often it is self-generated by the thermal agitation of electrons that make up the circuitry of a recording device or microphone, devices that commonly contain rare earth elements. In this sense, we might think of the noise floor as the result of millions of years of geologic formation.

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Her Noise Floor: Selection, Extraction, Exclusion (HNF) is a practice-based research project, which responds to digital audio material that is housed within the Her Noise Archive at UAL's Archives and Special Collections Centre. The project specifically engages with five of the six surviving radio programs from Melanie Clifford's Her Noise Series (2005).

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These programs were broadcast on Resonance FM between 16.30 - 16.45 GMT throughout November and December 2005, coinciding with the Her Noise (2005) exhibition at the South London Gallery, curated by Lina Dzuverovic and Anne Hilde Neset. An audible noise floor is present in the majority of the audio recordings featured in these five programs.

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Through the frameworks of critical data studies and extractivism, HNF aims to situate the noise floor as a highly localised, non-neutral, audible ecological material. Here, the noise floor is explored as both an archive of the in-situ acoustic properties of the rooms and places where these recordings were made and as a cartography of the geosocial conditions and curatorial decisions that (in)directly produced the Her Noise (2005) exhibition.

HNF asks, what does the presence of an audible noise floor in these archived recordings tell us about the social dynamics, economic conditions, climate, and geopolitical context in which the Her Noise (2005) exhibition originated?

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The project consists of two interrelated, process-driven parts:

1. Audio Datasets & Machine Learning Model: Two audio datasets (training and testing) were created from the Her Noise Series (2005), each split into two even classes: "signal" and "noise". Using these datasets, a machine learning model was built and trained to classify whether an audio file from the HNF dataset is "noise" or "signal".


2. HNF Interactive Noise Map: A browser-based sound map, which aims to function as an experimental archive and playable compositional tool; listeners can engage with “noise” predictions made by the model and explore the noise floor as ecological material.

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NAVIGATION TOOLS

You will encounter two types of buttons:

1. Yellow buttons play unprocessed samples from the HNF dataset that have been classified by the model as “noise” (denoted by the letters HNF).


2. Black buttons play the same samples, however, these samples have been denoised using Band Pass Filtering techniques (denoted by the letters BPF).

How to Use:

• Click a button to start/stop the audio file from playing on a loop.
• Refresh your browser to restore the map and return to this menu.

Text Pop-Ups:

• The text pop-ups that correspond to the yellow buttons contain information linked to local noise floor contributors (i.e. time, date, location, featured artists, etc).


• The text pop-ups that correspond to the black buttons contain information that links the noise floor to its planetary context (i.e. rare earth elements used in the recording technologies and place of manufacture, weather data, etc).

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In navigating the HNF Interactive Noise Map, I hope that listeners may actively explore the noise floor as a 'tangled cartography' (Hilde Neset, 2007) that holds traces of globalised value chains, microhistories, and geospheric systems.

The HNF Interactive Noise Map aims to pay homage to the mapping processes used by curators Lina Dzuverovic and Anne Hilde Neset, at the inception of Her Noise (2005), and act as a contemporary continuation of the Her Noise Archive—a place to reimagine, rearrange, and relisten to the material conditions of the past through emergent auditory cultures and technologies.

amias hanley x

www.amiashanley.com
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