︎

Lucy Strauss



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I have recently been found here:
︎ Göteborg
︎ London
︎ Cape Town




︎︎︎


 about 


Lucy Strauss is a musician and researcher drawing together viola performance, impovisation, composition & creative computing. With these practices, she seeks to deepen understanding of musicmaking with new and old technologies.

Lucy has performed in improvised and experimental music projects at Pony Books (Gothenburg), the TD Vancouver International Jazz Festival, Mixtophonics Festival, 8EAST Cultural Center & NOW Society (Vancouver), De Tanker (Amsterdam), Hundred Years Gallery (London), and Theatre Arts (Cape Town). She also enjoys contributing to interdisciplinary collaborations with fellow artists. Notably, she was the interaction designer for Denise Onen’s sounding the body as a sight at The Oscillations Exhibition 2024 (Akademie der Künste, Berlin). Also notably, Lucy has coded, composed and played for installations by artist Mia Thom at Everard Read Gallery, Act of Brutal Curation Gallery, and Eclectica Contemporary (Cape Town), as well as the 2022 Biennale de l'Art Africain Contemporain (Dakar). Lucy has presented workshops on composition (University of British Columbia), improvisation (Canadian Viola Society) and interactive music technology (Bowed Electrons Festival & Symposium). 

Recent highlights include a ︎student artist residency at TATE in London and a ︎studio recording residency at at The Studio for Electroacoustic Music, Akademie der Künste in Berlin.

Lucy learnt to love improvising at the MusicDance021 artist residency (ZA). She further developed her improvisation practice at 8EAST & Now Society (CA). She learnt to compose music at the University of Cape Town (BMus) and perform viola at the University of British Columbia (MMus). For two years, Lucy was an Artist in Residence at the University of Johannesburg (ZA). She is currently an AHRC CHASE -funded PhD researcher in Arts & Computational Technology at Goldsmiths, University of London (UK). She specialises in deep generative modeling of audio & electromyographic signals from viola performance, towards cross-modal muscle-sound machine translation in interactive music system design.

Lucy is based in Gothenburg (SE) and London (UK).



photo by Mia Thom photo by Mia Thom photo by Mia Thom


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PROJECTS

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MLMLMLM


What if listening happened through muscles instead of ears?


The Muscle-Listening Machine Learning Model for Live Music (MLMLMLM) is a novel deep generative model for muscle-sound machine translation. MLMLMLM is trained on a dataset of audio and EMG signals collected from my own body, during an artist residency at the Akademie der Künste, Berlin in July 2025.

VLA + MLMLMLM


MLMLMLM is situated within my music performance / composition / improvisation practice. I present MLMLMLM in live performance settings.

VLA + MLMLMLM is a performance piece featuring live viola and the Muscle-Listening Machine Learning Model for Live Music (MLMLMLM), a novel deep generative model for muscle-sound machine translation.

I interface with MLMLMLM through electromyographic sensors. MLMLMLM generates a sonic interpretation of live viola playing informed by the muscular bioelectricity of musical movement. The model ‘listens’ to the performance without ‘hearing’ sound.

With a human-centred approach to computing, VLA + MLMLMLM brings to light experiential dimensions of viola playing that are typically hidden from the audience. As such, we listen through emergent aesthetics of machine listening -- through muscles.  




past:
21/03/2026 | Delft, NL | For Love of the World: Digital Revolt @ TU Delft
26/02/2026 | Göteborg, SE | Open Process Night @ Skogen
29/11/2025 | Cape Town, ZA | Bowed Electrons Festival & Symposium





Gained In Translation

words prompt sounds

sounds prompt words 

photos by Robin Leverton
Gained In Translation is a music & text performance piece that I made in May 2025 for the Tech, Tea & Exchange residency at Tate.* The piece comprises different Machine Learning (ML) models strung together to form a recursive loop. The loop begins with human input: a short musical phrase that I play on viola in response to a text score.

text as score

I generate a text description from the viola audio, then generate audio using the generated text as a prompt... and the loop continues. Each performance is unique in how it unfolds and evolves because the initial input is improvised and there is a degree of stochasity (chance) in the ML model architectures.

failure to copy

cross-modal translation as meta-composition

I use translation between modalities (sound and text) as a meta-compositional technique, where the failure to reproduce gradually tansforms a single musical idea. While reproduction accuracy is an important element of evaluation in generative ML model training, the goal of artworks is usually not to reproduce, but to greate something physically or conceptually new. I place these two contradictory viewpoints on the nature and goals of reproduction side by side. In this way, Gained In Translation grew from the points of tension between artistic practice and technical practice.

what is gained in translation?

*The Tech, Tea & Exchange residency was supported by Anthropic and Gucci.
I provide a︎Github repository for this project.

examples


The piece is different each time it is performed, even if the initial audio is identical:


In the example videos, the generated text features references to battle and to music practices that I have no connection to. These references emerged as a result of the class labels of the pre-trained YAMNet model, and were likely further amplified by the recursive loop between the (also pre-trained) Stable Audio and Claude AI models featured in this piece. I would never have purposefully guided the piece towards violent themes and cultural appropriation. However, I have intentionally left these emergent themes in the piece to reveal nature of the models and their training datasets.

models & datasets


YAMNet - a sound classification model from TensorFlow

The model is trained to predict the most probable sound in an audio waveform. We can predict sounds from a pre-recorded sound file (as I do for the tech demo version of Gained in Translation) or from an incoming stream of audio (as I do in the Gained in Translaton performance).

dataset - "AudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos" https://research.google.com/audioset/
Claude 3.7 Sonnet - a large language model created by Anthropic

dataset - "Training data includes public internet information, non-public data from third-parties, contractor-generated data, and internally created data. When Anthropic's general purpose crawler obtains data by crawling public web pages, we follow industry practices with respect to robots.txt instructions that website operators use to indicate whether they permit crawling of the content on their sites. We did not train this model on any user prompt or output data submitted to us by users or customers." - https://www.anthropic.com/transparency
Stable Audio 2.0 (text & audio-to-audio)

dataset - "AudioSparx is an industry-leading music library and stock audio web site that brings together a world of music and sound effects from thousands of independent music artists, producers, bands and publishers in a hot online marketplace." https://www.audiosparx.com/