About

Luke Dahl is Associate Professor of Composition and Computer Technologies in the Music Department at the University of Virginia, where he teaches classes on music technology, audio signal processing, and music interaction design. His research is primarily situated in the interdisciplinary field of New Interfaces for Musical Expression (NIME), which investigates and explores the intersection of technology and musical practice through design, musical performance, and empirical research. He is especially interested in systems for digitally-mediated real-time music collaboration, and in the role of human gesture and movement in music. Dr. Dahl earned his PhD in Computer-Based Music Theory and Acoustics from the Center for Computer Research in Music and Acoustics (CCRMA) at Stanford University, and a bachelor’s in Electrical Engineering at the University of Michigan. At CCRMA, he was a founding member of the Stanford Laptop Orchestra and the Stanford Mobile Phone Orchestra. His musical works include SoundBounce for mobile phone orchestra, performed at NIME in Sydney, and TweetDreams for audience interaction and live Twitter data, premiered at the MiTo Settembre Musica Festival in Milan and performed in Oslo, San Francisco, Stony Brook, and at TEDx Silicon Valley. Before returning to academia, Dr. Dahl worked at the Joint E-mu/Creative Advanced Technology Center, developing reverb algorithms for the SoundBlasterLive sound card products and co-authoring patents on audio signal processing. He also worked at Apple on audio for iPod and laptop products. Recently, he has released alpha versions of two software libraries: Modosc, a Max library for processing motion-capture data in real-time for movement-based interactions, created in collaboration with Dr. Federico Visi; and Bf-Pd, a PureData library facilitating real-time data-sharing and collaboration between musicians in spontaneously formed ensembles, developed with Dr. Florent Berthaut at Université de Lille.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Human–computer interaction
• Machine Learning
• Medicine
• Multimedia
• Communication
• Knowledge management
• Simulation
• Pedagogy
• Engineering
• Psychotherapist
• Aesthetics
• Psychiatry
• Medical education
• Telecommunications
• Physical medicine and rehabilitation

Selected publications

• A review of concurrent sonified biofeedback in balance and gait training

  Journal of NeuroEngineering and Rehabilitation · 2025-02-26 · 3 citations

  reviewOpen accessSenior author

  BACKGROUND: Sonified biofeedback is a subtype of auditory biofeedback that conveys biological data through specific non-verbal sounds. It can be designed to provide augmented biomechanical feedback in near-real-time when provided as "concurrent" biofeedback. As a practice that developed spanning across engineering and the arts, sonified biofeedback can extend beyond simple tones and beeps, towards more fully incorporating music in movement training. Sonified biofeedback may leverage the motivational aspects of music in movement training, the neuroplasticity benefits demonstrated from participation in music-based interventions, and neurological auditory-motor coupling, all while providing task-relevant cues to facilitate motor (re)learning. Sonified biofeedback may also provide similar benefits as rhythmic cueing (e.g., rhythmic auditory stimulation), or added benefits because sonified biofeedback does not impose a strict isochronous rhythm when it follows rhythms that are driven by outputs of the motor control system. In this review paper, the unique opportunity presented by concurrent sonified biofeedback as a movement training tool for balance and gait is introduced and discussed. RESULTS AND DISCUSSION: This review paper brings together prior research from clinical, engineering, and artistic design sources using sonified biofeedback in balance and gait training across diverse end-users to highlight trends, reveal gaps in knowledge, and provide perspective for future work in the area. The goal was to review progress and critically assess research using sonified biofeedback during movement training for postural control or gait. 49 papers were selected based on their experimental investigation and statistical analyses of the effects of using sonified biofeedback to assist in movement training for feet-in-place balance tasks (20 papers) or gait tasks such as walking and running (29 papers). The sound design choices, experimental design features, and movement training results are summarized and reviewed. All but two studies reported at least one statistically significant positive effect of training with sonified biofeedback in biomechanical, clinical, or psychosocial measures. Conversely, only seven studies shared any negative effect on one biomechanical, clinical, or psychosocial measure (with five of these studies also reporting at least one other positive effect). After describing these encouraging findings, this review closes by sharing perspectives about future directions for designing and using sonified biofeedback in balance and gait training, and opportunities for more cohesive growth in this practice. One such suggestion is to pursue sonified designs and experimental designs that can translate to the neurorehabilitation field. This includes strategically selecting control groups and evaluation tasks to understand if improvements from training with one task transfer to additional relevant movement tasks. Additionally, it is important that future publications share details about the design processes and sound designs so researchers can more readily learn from one another. CONCLUSIONS: Overall, this review shares the positive impact of using sonified biofeedback in balance and gait training. This review highlights the evidence of existing successes and potential for even more impactful future positive effects from using sonified biofeedback to help diverse populations with a spectrum of balance and mobility challenges and goals.

  PublisherOA PDFDOI

• (Dis)Embodied mechatronic displays for telematic music performance

  Journal of New Music Research · 2024 · 1 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherDOI

• Final Report of the Open Source Science for Earth System Observatory Mission Data Processing Architecture Study

  Zenodo (CERN European Organization for Nuclear Research) · 2023-01-01

  reportOpen access

  The Open Source Science for Earth System Observatory (ESO) Mission Data Processing Architecture Study was sponsored by Kevin Murphy, Chief Science Data Officer of NASA’s Science Mission Directorate (SMD) and Program Manager for the Earth Science Division (ESD)Data Systems.

  PublisherDOI

• The Effect of Visualisation Level and Situational Visibility in Co-located Digital Musical Ensembles

  NIME 2022 · 2022-06-16 · 3 citations

  articleOpen accessSenior author

  Digital Musical Instruments (DMIs) offer new opportunities for collaboration, such as exchanging sounds or sharing controls between musicians. However, in the context of spontaneous and heterogeneous orchestras, such as jam sessions, collective music-making may become challenging due to the diversity and complexity of the DMIs and the musicians’ unfamiliarity with the others’ instruments. In particular, the potential lack of visibility into each musician’s respective contribution to the sound they hear, i.e. who is playing what, might impede their capacity to play together. In this paper, we propose to augment each instrument in a digital orchestra with visual feedback extracted in real-time from the instrument’s activity, in order to increase this awareness. We present the results of a user study in which we investigate the influence of visualisation level and situational visibility during short improvisations by groups of three musicians. Our results suggest that internal visualisations of all instruments displayed close to each musician’s instrument provide the best awareness.

  PublisherOA PDFDOI

• Understanding the Technological Practices and Needs of Music Therapists

  Proceedings of the ACM on Human-Computer Interaction · 2021 · 18 citations

  • Computer Science
  • Psychology
  • Psychotherapist

  Music therapists provide critical, evidence-based care to a diverse range of clients. However, despite their active role in empowering individuals affected by disability, stigma, grief, and trauma, music therapists remain understudied by the HCI community. We present the results of a mixed methods study of 10 interviewees and 20 survey respondents in the U.S., all of whom are practicing music therapists. Our results show that music therapists engage in technology-aided practices such as making personalized connections with clients, assisting in identity formation, encouraging musicking (music-making), and preserving legacies. Results also show that music therapists face key challenges such as environmental, societal, and financial constraints, including high workload, lack of awareness of the value of music therapy among the general community, and limited access to secure technologies for remote client care. In light of these challenges, we present a set of design implications for creating future technologies for music therapists. This work diverges from previous studies on music therapy technologies, which focus largely on interventions with music therapy clients, by highlighting the often-neglected perspectives from music therapists.

  PublisherDOI

• Real-Time Optical Motion Capture Balance Sonification System

  2020 · 4 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  In this study, we explored the effects of a motion capture-based real-time sonified biofeedback system on balance. We present the initial efforts towards developing a task-independent optical motion capture based real-time balance sonification system. Five healthy young adults (two female; 24 ± 2.65 years) stood on one foot before and during listening to sonified biofeedback that expressed information in real-time about the state of their balance. In two of five participants, interacting with our sonified biofeedback system resulted in increased "Margin of Stability", a metric indicative of how well the body center of mass is supported by a person's stance. This result indicates our system's initial promise towards training balance strategies. Qualitatively, the participants who increased the Margin of Stability during sonification reported enjoying the experience more and were more aware of changes in their behavior, compared to those who did not increase their Margin of Stability. We also learned that our sonification system has design elements that are incompatible with the stationary tasks in the present study, which will inform our next iteration of sonification design. Future work will examine sonifying balance in dynamic balance tasks, with the goal of aiding clinical balance training.

  PublisherDOI

• Adapting & Openness: Dynamics of Collaboration Interfaces for Heterogeneous Digital Orchestras

  2020 · 2 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Human–computer interaction

  Advanced musical cooperation, such as concurrent control of musical parameters or sharing data between instruments,has previously been investigated using multi-user instruments or orchestras of identical instruments. In the case of heterogeneous digital orchestras, where the instruments, interfaces, and control gestures can be very different, a number of issues may impede such collaboration opportunities. These include the lack of a standard method for sharing data or control, the incompatibility of parameter types, and limited awareness of other musicians' activity and instrument structure. As a result, most collaborations remain limited to synchronising tempo or applying effects to audio outputs. In this paper we present two interfaces for real-time group collaboration amongst musicians with heterogeneous instruments. We conducted a qualitative study to investigate how these interfaces impact musicians' experience and their musical output, we performed a thematic analysis of inter-views, and we analysed logs of interactions. From these results we derive principles and guidelines for the design of advanced collaboration systems for heterogeneous digital orchestras, namely Adapting (to) the System, Support Development, Default to Openness, and Minimise Friction to Support Expressivity.

  Publisher

• Developing real-time sonification with optical motion capture to convey balance-related metrics to dancers

  2019-09-25 · 8 citations

  article1st authorCorresponding

  This paper presents initial efforts in developing and evaluating a system for real-time sonification of balance during dance and other dynamic real-world movements. The system uses marker-based motion capture to calculate multiple balance metrics, which are used to control the parameters of a sound-generating process. This allows dancers and choreographers to explore balance as a compositional element and has potential for specific applications to improve strategies for maintaining balance in clinical populations. Initial feasibility tests suggest that subjects engage in enjoyable explorations that cause them to move beyond their normal range of activities. These habit-breaking explorations point towards promising use of the sonification system as a choreographic tool for dancers.

  PublisherDOI

• Real-Time Motion Capture Analysis And Music Interaction With The Modosc Descriptor Library

  Zenodo (CERN European Organization for Nuclear Research) · 2018-06-01 · 3 citations

  articleOpen accessSenior author

  We present modosc, a set of Max abstractions designed for computing motion descriptors from raw motion capture data in real time. The library contains methods for extracting descriptors useful for expressive movement analysis and sonic interaction design. modosc is designed to address the data handling and synchronization issues that often arise when working with complex marker sets. This is achieved by adopting a multiparadigm approach facilitated by odot and Open Sound Control to overcome some of the limitations of conventional Max programming, and structure incoming and outgoing data streams in a meaningful and easily accessible manner. After describing the contents of the library and how data streams are structured and processed, we report on a sonic interaction design use case involving motion feature extraction and machine learning.

  PublisherDOI

• Harmonic Wand: An Instrument For Microtonal Control And Gestural Excitation

  Zenodo (CERN European Organization for Nuclear Research) · 2018-06-01

  articleOpen accessSenior author

  The Harmonic Wand is a transducer-based instrument that combines physical excitation, synthesis, and gestural control. Our objective was to design a device that affords exploratory modes of interaction with the performer's surroundings, as well as precise control over microtonal pitch content and other concomitant parameters. The instrument is comprised of a hand-held wand, containing two piezo-electric transducers affixed to a pair of metal probes. The performer uses the wand to physically excite surfaces in the environment and capture resultant signals. Input materials are then processed using a novel application of Karplus-Strong synthesis, in which these impulses are imbued with discrete resonances. We achieved gestural control over synthesis parameters using a secondary tactile interface, consisting of four force-sensitive resistors (FSR), a fader, and momentary switch. As a unique feature of our instrument, we modeled pitch organization and associated parametric controls according to theoretical principles outlined in Harry Partch's "monophonic fabric" of Just Intonation—specifically his conception of odentities, udentities, and a variable numerary nexus. This system classifies pitch content based upon intervallic structures found in both the overtone and undertone series. Our paper details the procedural challenges in designing the Harmonic Wand.

  PublisherDOI

Frequent coauthors

• Florent Berthaut

  Centre National de la Recherche Scientifique

  9 shared

• Federico Visi

  Luleå University of Technology

  2 shared

• Ge Wang

  Rensselaer Polytechnic Institute

  2 shared

• Antonia M. Zaferiou

  Stevens Institute of Technology

  2 shared

• Jon Bellona

  2 shared

• Amy LaViers

  2 shared

• Jorge Herrera

  Autonomous University of Aguascalientes

  2 shared

• Christopher B. Knowlton

  Rush University Medical Center

  2 shared