Serdar, Bilge; Tidemann, Aleksander & Jensenius, Alexander Refsum
(2026).
Performative Togetherness in networked music–dance performance.
International Journal of Performance Arts and Digital Media.
ISSN 1479-4713.s. 1–22.
doi: 10.1080/14794713.2026.2657654.
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This article examines how performative togetherness is constructed in a technologically mediated music–dance performance environment. While togetherness is often associated with co-presence and effortless flow, networked contexts introduce perceptual reconfiguration that makes it fragile and effortful. We present the research concert Telematic@PopSenteret, a networked performance involving dancers and musicians distributed across two floors in a museum, connected through audio–video transmission. Using interviews and audio-video recordings, we explore how latency, fractured perception, and the absence of bodily cues transformed the performers’ coordination and induced cognitive strain. We discuss how performative togetherness – defined as the sense of shared meaning-making that emerges through attentional, sensory, and adaptive negotiation – was maintained during the performance.
Serdar, Bilge; Tidemann, Aleksander & Jensenius, Alexander Refsum
(2026).
Mixed Method Audio-Video Analyses of Felt Togetherness in a Networked Music-Dance Performance.
I Guyot, Patrice & Bosselut, Gregoire (Red.),
MOCO '26: Proceedings of the 10th International Conference on Movement and Computing.
Association for Computing Machinery (ACM).
ISSN 9798400725005.
doi: 10.1145/3802842.3802884.
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This paper presents a mixed-method approach to investigating the felt experience of a networked music–dance performance. Using this approach, we explore how a sense of togetherness emerges even when performers are distributed across distant spaces. Drawing on data from the Telematic@Popsenteret research concert, we propose a three-layered analytical framework that integrates computer-based audio-video analyses with qualitative observation, and post-performance interviews. By combining sound–motion synchrony and temporal coupling with performers’ and audience members’ experiential accounts, we examine how dancers and musicians negotiate coordination and shared presence in a telematic environment. Our findings suggest that togetherness in such settings is not merely a matter of temporal alignment, but a dynamically constructed, multimodal process involving perceptual adaptation, cross-modal attunement, and affective awareness.
Simionato, Riccardo & Tidemann, Aleksander
(2025).
Compressing Neural Network Models of Audio Distortion Effects Using Knowledge Distillation Techniques.
Proceedings of the 1st AES International Conference on Artificial Intelligence and Machine Learning for Audio (AIMLA).
1(1),
s. 117–126.
doi: https:/aes2.org/publications/elibrary-page/?id=23001.
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Neural networks have proven to be effective for modeling analog audio effects using a black-box approach. However, few can guarantee lightweight solutions suitable for real-time environments where the models must run concurrently on consumer-grade equipment. This paper explores knowledge distillation techniques for compressing recurrent neural network models for audio distortion effects, aiming to produce computationally efficient solutions with high accuracy that maintain compact model size. In particular, we consider an audio-to-audio LSTM architecture for regression tasks where small networks are trained to mimic the internal representations of larger networks, known as feature-based knowledge distillation. The evaluation was conducted on three different audio distortion effect datasets with experiments on both parametric and non-parametric data. The results show that distilled models are more accurate than non-distilled models with equal parameter size, especially for models that exhibit higher error rates. Furthermore, we observe that smaller complexity gaps between student and teacher models yield greater improvements in non-parametric cases.
Fasciani, Stefano; Simionato, Riccardo & Tidemann, Aleksander
(2024).
A Universal Tool for Generating Datasets from Audio Effects,
Proceedings of the Sound and Music Computing Conference 2024.
SMC Network.
ISSN 9789893520758.s. 500–507.
doi: 10.5281/zenodo.14362474.
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This paper presents an open-source software tool that simplifies and automates the generation of accurate and reproducible datasets from audio effect devices. The tool is universal as it is designed to work with both software and hardware devices, presenting analog or digital interfaces. Specifically, this tool facilitates the process of recording the processed sound while varying effect control parameters, with four different paradigms offered. The tool has an interactive graphical user interface that facilitates interfacing audio effects and configuring dataset generation options. The generated dataset is organized as a collection of sample-accurate files, which makes further processing or analysis with offline scripts simpler. The paper details the tool’s design and its extensive set of functionalities, which are informed by research in machine learning modeling of audio effects. While discussing the tool’s applications, we also explore its potential in other contexts.
The two largest universities in Norway started a new joint international master’s program called “Music, communication and technology” (MCT) in 2018. MCT constitutes the framework for developing a new shared learning space (The Portal) for physical-virtual interaction, musical communication, and collaboration.The COVID-19 pandemic has illuminated several challenges within hybrid teaching concerning the design of learning spaces and related pedagogical and technical issues. In our case, we identify challenges in delivering a hybrid learning space with spatial audio and discuss a method to implement such a solution in the context of spatial audio and ambisonics workshops developed for the MCT master’s program. We propose a stable but flexible technical solution for decoding higher-order ambisonics audio in two remote physical locations and into virtual environments in real-time, creating a continuum between the physical, hybrid, and online learning spaces mediated by technology.
Neural networks have proven to be effective for modeling analog audio effects using a black-box approach. However, few can guarantee lightweight solutions suitable for real-time environments where the models must run concurrently on consumer-grade equipment. This paper explores knowledge distillation techniques for compressing recurrent neural network models for audio distortion effects, aiming to produce computationally efficient solutions with high accuracy that maintain compact model size. In particular, we consider an audio-to-audio LSTM architecture for regression tasks where small networks are trained to mimic the internal representations of larger networks, known as feature-based knowledge distillation. The evaluation was conducted on three different audio distortion effect datasets with experiments on both parametric and non-parametric data. The results show that distilled models are more accurate than non-distilled models with equal parameter size, especially for models that exhibit higher error rates. Furthermore, we observe that smaller complexity gaps between student and teacher models yield greater improvements in non-parametric cases.
This digital forum explores artistic collaboration through online platforms, examining how technology can facilitate sustainable and eco-friendly ways of creating music collectively. Participants will gain insights into artist-driven initiatives and practical tools for creative co-creation, regardless of geographical distance. Following three short presentations from invited contributors, there will be an open dialogue and exchange of experiences, allowing ULYSSES artists to share their own projects.
Göksülük, Bilge Serdar; Tidemann, Aleksander & Jensenius, Alexander Refsum
(2024).
Telematic Testing: One Performance in Three Locations.
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Støckert, Robin & Tidemann, Aleksander
(2022).
Hybrid Learning Spaces with Spatial Audio.
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Tidemann, Aleksander & Lartillot, Olivier
(2021).
Interactive tools for exploring performance patterns in hardanger fiddle music.
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Tidemann, Aleksander
(2021).
Exploring Hardanger fiddle performance patterns through interactive tools.
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Tidemann, Aleksander; Lartillot, Olivier & Johansson, Mats Sigvard
(2021).
Towards New Analysis And Visualization Software For Studying Performance Patterns in Hardanger Fiddle Music.
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Analyzing musical performances is a challenging and emergent field of computational music research, aiming to reveal performance patterns and link them to musical contexts. There exists a modest amount of computational research on Hardanger fiddle performances. The MIRAGE research project is currently contributing to this scientific body, developing advanced MIR frameworks that build on recent musicological research. This paper presents the development and evaluation of two Max/MSP/Jitter software applications for music analysis and data visualization that integrate contemporary research perspectives on the complex rhythmical structuring of springar performances, investigating how we can design user-friendly computational tools that explore performance patterns in Hardanger fiddle music, in collaboration with MIRAGE.
Based on a small questionnaire and a few operational tests, the study shows an interest in more effective software tools capable of revealing complex interrelations between musical dimensions in Hardanger fiddle performances. Additionally, the study highlights design considerations for tools aiming to increase the availability of computational music research in the field of musicology, such as cross-compatibility and integrated features that actively facilitate nuanced interpretation processes.
