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This paper describes the development of two musical instrument prototypes developed to explore how non-haptic music technologies can be accessed from a web browser and how they can offer accessibility for people with low fine motor skills. Two approaches to browser-based motion capture were developed and tested during an iterative design process. This was followed by observational studies of two user groups: one with low fine motor skills and one with normal motor skills. Contrary to our expectations, we found that avoiding the use of buttons and mice did not make the apps more accessible for the participants with low fine motor skills. Furthermore, motion speed was considered more important for people with low motor skills than the size of the control action. The most important finding is that browser-based musical instruments using sensor-based and video-based motion tracking are not only feasible but allow for reaching much larger groups of people than previously possible. This may ultimately lead to both more personalized and accessible musical experiences.

The topic of gesture has received growing attention among music researchers over recent decades. Some of this research has been summarized in anthologies on "musical gestures", such as those by Gritten and King (2006), Godøy and Leman (2010), and Gritten and King (2011). There have also been a couple of articles reviewing how the term gesture has been used in various music-related disciplines (and beyond), including those by Cadoz and Wanderley (2000) and Jensenius et al. (2010). Much empirical work has been performed since these reviews were written, aided by better motion capture technologies, new machine learning techniques, and a heightened awareness of the topic. Still there are a number of open questions as to the role of gestures in music performance in general, and in ensemble performance in particular. This chapter aims to clarify some of the basic terminology of music-related body motion, and draw up some perspectives of how one can think about gestures in ensemble performance. This is, obviously, only one way of looking at the very multifaceted concept of gesture, but it may lead to further interest in this exciting and complex research domain.

The paper presents the Musical Gestures Toolbox (MGT) for Python, a collection of modules targeted at researchers working with video recordings. The toolbox includes video visualization techniques such as creating motion videos, motion history images, and motiongrams. These visualizations allow for studying video recordings from different temporal and spatial perspectives. The toolbox also includes basic computer vision methods, and it is designed to integrate well with audio analysis toolboxes. The MGT was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly ( p<0.05 ) outperforms using fine-tuning as the recalibration technique.

This paper addresses environmental issues around NIME research and practice. We discuss the formulation of an environmental statement for the conference as well as the initiation of a NIME Eco Wiki containing information on environmental concerns related to the creation of new musical instruments. We outline a number of these concerns and, by systematically reviewing the proceedings of all previous NIME conferences, identify a general lack of reflection on the environmental impact of the research undertaken. Finally, we propose a framework for addressing the making, testing, using, and disposal of NIMEs in the hope that sustainability may become a central concern to researchers.

Music researchers work with increasingly large and complex data sets. There are few established data handling practices in the field and several conceptual, technological, and practical challenges. Furthermore, many music researchers are not equipped for (or interested in) the craft of data storage, curation, and archiving. This paper discusses some of the particular challenges that empirical music researchers face when working towards Open Research practices: handling (1) (multi)media files, (2) privacy, and (3) copyright issues. These are exemplified through MusicLab, an event series focused on fostering openness in music research. It is argued that the "best practice" suggested by the FAIR principles is too demanding in many cases, but "good enough practice" may be within reach for many. A four-layer data handling "recipe" is suggested as concrete advice for achieving "good enough practice" in empirical music research.

Orbitofrontal cortex (OFC) is implicated in multiple cognitive processes, including inhibitory control, context memory, recency judgment, and choice behavior. Despite an emerging understanding of the role of OFC in memory and executive control, its necessity for core working memory (WM) operations remains undefined. Here, we assessed the impact of OFC damage on interference effects in WM using a Recent Probes task based on the Sternberg item-recognition task (1966). Subjects were asked to memorize a set of letters and then indicate whether a probe letter was presented in a particular set. Four conditions were created according to the forthcoming response ("yes"/"no") and the recency of the probe (presented in the previous trial set or not). We compared behavioral and electroencephalography (EEG) responses between healthy subjects (n = 14) and patients with bilateral OFC damage (n = 14). Both groups had the same recency pattern of slower reaction time (RT) when the probe was presented in the previous trial but not in the current one, reflecting the proactive interference (PI). The within-group electrophysiological results showed no condition difference during letter encoding and maintenance. In contrast, event-related potentials (ERPs) to probes showed distinct within-group condition effects, and condition by group effects. The response and recency effects for controls occurred within the same time window (300-500 ms after probe onset) and were observed in two distinct spatial groups including right centro-posterior and left frontal electrodes. Both clusters showed ERP differences elicited by the response effect, and one cluster was also sensitive to the recency manipulation. Condition differences for the OFC group involved two different clusters, encompassing only left hemisphere electrodes and occurring during two consecutive time windows (345-463 ms and 565-710 ms). Both clusters were sensitive to the response effect, but no recency effect was found despite the behavioral recency effect. Although the groups had different electrophysiological responses, the maintenance of letters in WM, the evaluation of the context of the probe, and the decision to accept or reject a probed letter were preserved in OFC patients. The results suggest that neural reorganization may contribute to intact recency judgment and response after OFC damage.

The brain responds to violations of expected rhythms, due to extraction- and prediction of the temporal structure in auditory input. Yet, it is unknown how probability of rhythm violations affects the overall rhythm predictability. Another unresolved question is whether predictive processes are independent of attention processes. In this study, EEG was recorded while subjects listened to rhythmic sequences. Predictability was manipulated by changing the stimulus-onset-asynchrony (SOA deviants) for given tones in the rhythm. When SOA deviants were inserted rarely, predictability remained high, whereas predictability was lower with more frequent SOA deviants. Dichotic tone-presentation allowed for independent manipulation of attention, as specific tones of the rhythm were presented to separate ears. Attention was manipulated by instructing subjects to attend to tones in one ear only, while keeping the rhythmic structure of tones constant. The analyses of event-related potentials revealed an attenuated N1 for tones when rhythm predictability was high, while the N1 was enhanced by attention to tones. Bayesian statistics revealed no interaction between predictability and attention. A right-lateralization of attention effects, but not predictability effects, suggested potentially different cortical processes. This is the first study to show that probability of rhythm violation influences rhythm predictability, independent of attention.

Decades of electrophysiological research on top-down control converge on the role of the lateral frontal cortex in facilitating attention to behaviorally relevant external inputs. However, the involvement of frontal cortex in the top-down control of attention directed to the external versus internal environment remains poorly understood. To address this, we recorded intracranial electrocorticography while subjects directed their attention externally to tones and responded to infrequent target tones, or internally to their own thoughts while ignoring the tones. Our analyses focused on frontal and temporal cortices. We first computed the target effect, as indexed by the difference in high frequency activity (70-150 Hz) between target and standard tones. Importantly, we then compared the target effect between external and internal attention, reflecting a top-down attentional effect elicited by task demands, in each region of interest. Both frontal and temporal cortices showed target effects during external and internal attention, suggesting this effect is present irrespective of attention states. However, only the frontal cortex showed an enhanced target effect during external relative to internal attention. These findings provide electrophysiological evidence for top-down attentional modulation in the lateral frontal cortex, revealing preferential engagement with external attention.

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system’s performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

A techno-cognitive look at how new technologies are shaping the future of musicking. “Musicking” encapsulates both the making of and perception of music, so it includes both active and passive forms of musical engagement. But at its core, it is a relationship between actions and sounds, between human bodies and musical instruments. Viewing musicking through this lens and drawing on music cognition and music technology, Sound Actions proposes a model for understanding differences between traditional acoustic “sound makers” and new electro-acoustic “music makers.” What is a musical instrument? How do new technologies change how we perform and perceive music? What happens when composers build instruments, performers write code, perceivers become producers, and instruments play themselves? The answers to these pivotal questions entail a meeting point between interactive music technology and embodied music cognition, what author Alexander Refsum Jensenius calls “embodied music technology.” Moving between objective description and subjective narrative of his own musical experiences, Jensenius explores why music makes people move, how the human body can be used in musical interaction, and how new technologies allow for active musical experiences. The development of new music technologies, he demonstrates, has fundamentally changed how music is performed and perceived.

The ability to use predictive information to guide perception and action relies heavily on the prefrontal cortex (PFC), yet the involvement of its subregions in predictive processes remains unclear. Recent perspectives propose that the orbitofrontal cortex (OFC) generates predictions about perceptual events, actions, and their outcomes while the lateral prefrontal cortex (LPFC) is involved in prospective functions, which support predictive processes, such as selective attention, working memory, response preparation or inhibition. To further delineate the roles of these PFC areas in predictive processing, we investigated whether lesions would impair the ability to build predictions of future events and detect deviations from expected regularities. We used an auditory deviance detection task, in which the structural regularities of played tones were controlled at two hierarchical levels by rules defined at a local (i.e., between tones within sequences) and global (i.e., between sequences) level. We have recently shown that OFC lesions affect detecting prediction violations at two hierarchical levels of rule abstraction, i.e., altered MMN and P3a to local and simultaneous local + global prediction violations (https://doi.org/10.7554/eLife.86386). Now, we focus on the task's predictive aspect and present the latest results showing the involvement of PFC subregions in anticipation of deviances informed by implicit predictive information. Behavioral data shows that deviance expectancy induced faster deviance detection in healthy adults (n=22), suggesting that participants track a state space representation of the task and anticipate upcoming deviant sequences. The analysis of EEG data from patients with focal lesions to the OFC (n = 12) or LPFC (n = 10), and SEEG from the same areas in patients with epilepsy (n = 7), revealed interesting differences. Healthy adults (n = 15) showed modulations of the Contingent Negative Variation (CNV) – a marker of anticipatory activity - tracking the expectancy of deviant tone sequences. However, patients with OFC lesions lacked CNV sensitivity to the predictive context, while patients with LPFC lesions showed moderate sensitivity compared to healthy adults. These results were further supported by intracranial recordings, which revealed expectancy modulation of the high-frequency broadband signal from electrodes in OFC and LPFC, with an earlier latency of activity modulation for the OFC and a later one for the LPFC. Altogether, the complementary approach from behavioral, intracerebral EEG, scalp EEG, and causal lesion data provides compelling evidence for the distinct engagement of the two prefrontal areas in predicting future events and signaling deviations.

This workshop is targeted at students and researchers working with video recordings. Even though the workshop will be based on quantitative tools, the aim is to provide solutions for qualitative research. This includes visualization techniques such as motion videos, motion history images, and motiongrams, which, in different ways, allow for looking at video recordings from different temporal and spatial perspectives. It also includes basic computer vision analysis modules, such as extracting quantity and centroid of motion, and using such features in analysis. The participants will learn to use the Musical Gestures Toolbox for Python, a collection of high-level modules for easily generating all of the above-mentioned visualizations and analyses. This toolbox was initially developed for analyzing music-related body motion but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, psychology, medicine, and educational sciences.

This paper describes the development of two musical instrument prototypes developed to explore how non-haptic music technologies can be accessed from a web browser and how they can offer accessibility for people with low fine motor skills. Two approaches to browser-based motion capture were developed and tested during an iterative design process. This was followed by observational studies of two user groups: one with low fine motor skills and one with normal motor skills. Contrary to our expectations, we found that avoiding the use of buttons and mice did not make the apps more accessible for the participants with low fine motor skills. Furthermore, motion speed was considered more important for people with low motor skills than the size of the control action. The most important finding is that browser-based musical instruments using sensor-based and video-based motion tracking are not only feasible but allow for reaching much larger groups of people than previously possible. This may ultimately lead to both more personalized and accessible musical experiences.

In this study, we tested the causal involvement of the OFC in noticing breaches of predictions (i.e., PEs) at different hierarchical levels of task structural complexity. With this aim, we examined the event-related potentials (ERPs) of patients with focal OFC lesions and healthy adults while performing an auditory local-global oddball paradigm. Altogether, we found that after OFC damage, low-level PEs (i.e., processing of stimuli that are unpredicted at the local level) and combined low- and high-level PEs (i.e., processing of stimuli that are unpredicted at both the local and global level) were impacted. However, the processing of standard tones was not affected. We conclude that the OFC may contribute to a top-down process that modulates the deviance detection system in the primary auditory cortices, and may be involved in connecting PEs at lower hierarchical areas with predictions at higher areas. The study sheds new light on the poorly explored deficits of hierarchical auditory prediction in patients with damaged OFC.

I denne presentasjonen vil jeg presentere hvordan vi gjennom årene har utviklet tre komplette nettkurs ved Universitetet i ̽����ѡ: Music Moves (2016), Motion Capture (2022) og Pupillometry (2023). Fokuset vil ligge på muligheter og utfordringer i video i utdanningssammenheng.

The Musical Gestures Toolbox for Python is a collection of tools for video visualization and video analysis.

In this workshop, hosted by the three NIME environmental officers, participants will be introduced to the NIME Eco Wiki, a repository for addressing environmental and sustainability issues within the NIME community. During the workshop, the participants will discuss how practices on the communal as well as the individual level may become more sustainable and they will create new additions and ideas for the Wiki.

The paper presents the Musical Gestures Toolbox (MGT) for Python, a collection of modules targeted at researchers working with video recordings. The toolbox includes video visualization techniques such as creating motion videos, motion history images, and motiongrams. These visualizations allow for studying video recordings from different temporal and spatial perspectives. The toolbox also includes basic computer vision methods, and it is designed to integrate well with audio analysis toolboxes. The MGT was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.

A presentation of labs and studios at the Department of Musicology and RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion at the University of ̽����ѡ.

Academics need to be visible online. If you don’t publish and disseminate your research, it won’t have an impact. So it is in our own interest to have up-to-date personal pages with information about what we do. I would argue that it is also in the interest of universities that their employee’s personal pages are up-to-date and look good.

This paper addresses environmental issues around NIME research and practice. We discuss the formulation of an environmental statement for the conference as well as the initiation of a NIME Eco Wiki containing information on environmental concerns related to the creation of new musical instruments. We outline a number of these concerns and, by systematically reviewing the proceedings of all previous NIME conferences, identify a general lack of reflection on the environmental impact of the research undertaken. Finally, we propose a framework for addressing the making, testing, using, and disposal of NIMEs in the hope that sustainability may become a central concern to researchers.

The accurate localization of electrodes in relationship to the brain’s anatomy is the foundation of the spatial resolution of intracranial EEG recordings. However, in “difficult cases“ the localization needs to be done manually since automatic methods fails, e.g high density arrays up to 3 mm inter-electrode distance, overlapping electrodes, low resolution CT images, or connection cables ovelaying grids. Here, we present a new automatic method that models a flexible array of electrodes and fits it to the artifacts observed in post implantation CT images. We evaluated data from 18 adult patients with drug resistant epilepsy implanted with depth electrodes and/or subdural grids (18 patients, 3261 electrodes). The automatic method was contrasted against manual localization. The main processing steps (Fig. 1 A) were: Thresholding and selection of a cloud of CT voxels containing the electrode artifacts Assembling a model of the grid (depth) array of electrodes Fitting the model to a smooth surface (line) approximation of CT artifacts Fitting the model to the cloud of voxels by minimizing the energy function E = -Ec + Et + Ed Ec was the gaussian weighted spatial correlation between the electrodes and the cloud of voxels. Et penalized the translation of electrodes, and Ed the deformation of a spring grid connecting the electrodes. Automatic localization resulted to be more precise than manual selection, observed as a significant reduction of the inter-electrode distance variance (Fig. 1 B). We provide a robust method for intracranial electrode localization that is applicable to “difficult cases” were previous automatic methods fail (Fig. 1 C). The method was implemented in the open-source iElectrodes toolbox and is available to the research community (Blenkmann et al., 2017).

Sensory consequences of actions are predicted by the brain via an internal forward model to prepare sensory cortical areas, referred to as motor prediction. In a similar vein, the predictive coding framework suggests that perception is based on internal models making predictions about sensory events, based on statistical probabilities of the stimuli. In the current study we investigated action-based sensory predictions. We used a self-paced, two-choice random generation task, infrequently inducing deviant outcomes of voluntary action. Participants repeatedly pressed a right and a left button normatively associated with a 70 ms long 1 kHz and 2 kHz tone, respectively. Occasional deviants occurred, inverting the learned button-tone association. Participants were instructed that their button presses should be random, at a regular but self-paced tempo of one press per 1-2 s, and that they should press both buttons with equal probability. They were informed that the tones are task-irrelevant. We used intracranial EEG (iEEG) data recorded from 10 adult patients with electrodes localized in frontal and temporal lobes. The patients had drug resistant epilepsy and were undergoing presurgical monitoring via implanted stereotactic electrodes. Electrode coordinates and anatomical labels were obtained from coregistered MRI and CT images using iElectrodes toolbox. Initial results indicate that violations of action intentions modulated high frequency band activity (HFA, 75-145 Hz) in distributed brain regions including temporal and prefrontal cortices.

The neural network underlying human auditory deviance detection is not fully understood. To address this, we recorded intracranial EEG from 22 adult patients with drug resistant epilepsy undergoing presurgical monitoring who had depth electrodes implanted in all brain lobes (1193 channels in total). Patients passively heard a stream of bilaterally presented tones while reading. We used the Optimum-1 paradigm, that consisted of 300 standard tones interleaved with 300 randomly presented deviant tones per block. Patients completed between 3 to 10 blocks. Deviant tones differed from standards in: 1) intensity (louder or softer), 2) frequency (higher or lower), 3) sound source location (right or left), 4) a shorter duration, or 5) a silent gap in the middle of the tone (Näätänen et al., 2004). Electrode coordinates were obtained from coregistered MRI and CT images using iElectrodes toolbox (Blenkmann et al., 2017). Channels were bipolar referenced and high frequency band activity (HFA) analytic amplitude signal was obtained using the Hilbert transform (75-145 Hz). Compared to the baseline period, significant HFA responses to tones in general were observed in 31% of the channels. We used an ANOVA to quantify the HFA response variance across trials that could be explained by the different factors of the stimuli: Intensity, Laterality, Frequency, Duration and Gap. We estimated the amount of explained variance by using ?2 (Siegel et al., 2015). Eighteen % of the channels showed a significant increase of the condition-specific explained variance. Some channels showed condition-specific activations to one particular deviant, while others showed activations to a combination of two or more deviants. The channels showing responses to tones in general and condition-specific effects were mostly observed bilaterally in temporal cortex. Frontal, anterior cingulate, and parietal cortices were also involved to a lesser extent. Our results, in line with the predictive coding framework, reveal that a distributed brain network is involved in auditory processing and deviance detection.

What is an instrument in our increasingly electrified world? In this talk I will present a set of theoretical building blocks from my forthcoming book on "musicking in an electronic world". At the core of the argument is the observation that the introduction of new music technologies has led to an increased separation between action and sound in musical performance. This has happened gradually, with pianos and organs being important early examples of instruments that introduced mechanical components between the performer and resonating objects. Today's network-based instruments represent an extreme case of a spatiotemporal dislocation between action and sound. They challenge our ideas of what an instrument can be, who can perform on them, and how they should be analyzed. In the lecture I will explain how we can use the concepts of action-sound couplings and mappings to structure our thinking about such instruments. This will be used at the heart of a new organology that embraces the qualities of both acoustic and electroacoustic instruments.

This presentation will summarize findings from my research into music-related micromotion. This includes the smallest human motion that we can perform and perceive, typically measured at at a scale of millimeters. We have carried out a series of studies of such micromotion, in which people have been asked to try to stand still on the floor, both in silence and with (musical) sound. By measuring their bodily responses with different types of motion tracking and physiological devices we find a number of similarities between people's quantity and quality of motion. This has been the starting point for exploring the use of micromotion in musical practice, what I call 'sonic microinteraction'. This includes standstill performances with interactive sound and light. It also includes several installations with our ensemble of self-playing guitars. These are hybrid instruments, using digital sound-production through acoustically resonating guitars. They are controlled through inverse microinteraction, meaning that you need to focus on standing still to produce any sound. This challenges our traditional understanding of the affordance of musical instruments, and opens for both artistically and scientifically interesting perspectives.

What is an instrument in our increasingly electrified world? In this talk I will present a set of theoretical building blocks from my forthcoming book on "musicking in an electronic world". At the core of the argument is the observation that the introduction of new music technologies has led to an increased separation between action and sound in musical performance. This has happened gradually, with pianos and organs being important early examples of instruments that introduced mechanical components between the performer and resonating objects. Today's network-based instruments represent an extreme case of a spatiotemporal dislocation between action and sound. They challenge our ideas of what an instrument can be, who can perform on them, and how they should be analyzed. In the lecture I will explain how we can use the concepts of action-sound couplings and mappings to structure our thinking about such instruments. This will be used at the heart of a new organology that embraces the qualities of both acoustic and electroacoustic instruments.

This talk will highlight links between music and human movement, aiming at providing insight into crucial aspects of human perception, cognition, and sensorimotor systems. It will analyze responses to a wide range of music and sound features, exploiting concepts such as the groove, embodied music cognition, and entrainment. Victor will be glad to discuss potential implications of movement-analysis research for embodiment perspectives on technologically enabled conceptual learning.

Virtuosity in music performance is often associated with fast, precise, and efficient sound-producing movements. The generation of such highly skilled movements involves complex joint and muscle control by the central nervous system, and depends on the ability to anticipate, segment, and coarticulate motor elements, all within the biomechanical constraints of the human body. When successful, such motor skill should lead to what we characterize as fluency in musical performance. Detecting typical features of fluency could be very useful for technology-enhanced learning systems, assisting and supporting students during their individual practice sessions by giving feedback and helping them to adopt sustainable movement patterns. In this study, we propose to assess fluency in musical performance as the ability to smoothly and efficiently coordinate while accurately performing slow, transitionary, and rapid movements.

Virtuosity in music performance is often associated with fast, precise, and efficient sound-producing movements. The generation of such highly skilled movements involves complex joint and muscle control by the central nervous system, and depends on the ability to anticipate, segment, and coarticulate motor elements, all within the biomechanical constraints of the human body. When successful, such motor skill should lead to what we characterize as fluency in musical performance. Detecting typical features of fluency could be very useful for technology-enhanced learning systems, assisting and supporting students during their individual practice sessions by giving feedback and helping them to adopt sustainable movement patterns. In this study, we propose to assess fluency in musical performance as the ability to smoothly and efficiently coordinate while accurately performing slow, transitionary, and rapid movements.

An intensive PhD-level training course on sound and motion analysis with experts in sound and music computing from the Nordic countries.

Seminar with collaborators from University of Technology, Japan: Shigeki Nakauchi <https://www.tut.ac.jp/english/schools/faculty/cs/169.html> og Tetsuto Minami <https://www.tut.ac.jp/english/schools/faculty/eiiris/573.html> and their students.

The Musical Gestures Toolbox (MGT) is a Matlab toolbox for analysing music-related body motion, using sets of audio, video and motion capture data as source material.