Future Music #3 , Royal Northern College of Music

AV piece from my musique concrete and machine learning research residency with NOVARS, centre for innovation in sound, University of Manchester in collaboration with PRiSM. The piece premiered at PRiSM FutureMusic3 event, Royal Northern College of Music, June 2020 alongside works from our UNSUPERVISED machine learning for music working group.

About the piece_

A sound object has an aura.

Taking the starting point of the sound object, a sonic fragment or atom of authentic matter, what happens to this materiality when processed by a neural network? What new sonic materials and aesthetics will emerge? Can the AI system project newly distilled hybrid forms or will the process of data compression result in a lo-fi statistical imitation?

For this piece, my first experiment with neural synthesis, I sought to collide the two disciplines of musique concrete and machine learning to take the listener on a journey through the process of training a SampleRNN model. 

A tale of two states, AURA MACHINE begins with the training data, the original source material comprising the concrete dataset. Field recordings were categorised into distinct classes; ‘Echoes of Industry’ (Manchester mill spaces), ‘Age of Electricity’ (DIY technology, noise & machinery) and ‘Materiality’ glass fragments and metal sound sculptures. The second state is the purely generated AI output audio.  

Can a machine produce an aura?”

‘The genuineness of a thing is the quintessence of everything about it since its creation that can be handed down, from its material duration to the historical witness that it bears.’

The Work of Art in the Age of Mechanical Reproduction, Walter Benjamin

The piece takes its name from the Walter Benjamin essay ‘The Work of Art in the Age of Mechanical Reproduction’ questioning the aura of an object and its authenticity when reproduced. I thought it interesting to consider this from our 2021 creative AI context, and apply this to sonic aesthetics so my piece asks “What happens to the sound object when processed by a neural network”?  Interested in exposing hidden systems and technological processes, for this first experiment the form of the piece sought to guide the listener through the process of training a neural network:


silver symbols on a black background

AURA MACHINE Symbolic language, StyleGAN trained on dataset of electronic circuit and alchemical symbols

I purposefully kept the output AI material as pure as possible for the listener to really hear the textural makeup of the SampleRNN audio – to witness the changes of state (and low sample rate) that had occurred through the system.

So can a machine produce an aura ? I would say yes. I was enchanted by the lo-fi sonic world of the output material, I felt it had a distinct almost analogue warmth and depth ironically reminiscent of early musique concrete tape recordings. This ‘lofi-ness’ is due to the low sample rate (16K) required for training the model and can be a challenge – I however decided to embrace the feel of this for the piece. Having just begun working with this material, I intend to undertake more experiments and analysis but at this stage I know that the aura of the soundworld and my connection to the material has captivated my sense of the electrical imaginary.

The process definitely created new artefacts, blended material atmospheres and improvised collaged architectures which for myself pose exciting questions for composition. These features were not pale imitations or reproductions of the training data but held their own space and authenticity as new object forms and projections.


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