Publications are important for researchers. Therefore deciding on who should be named as author for an academic publication is a topic that often leads to discussions. Also the ordering of the author names in a publication is a topic for heated debate, and particularly when you work in interdisciplinary teams with different traditions, as can be seen in the version from PhD Comics below.
Here is a task I have developed as a point of departure for discussing this issue in research groups. This is a task we have used successfully at RITMO, and hopefully others can make use of it too.
Consider the following scenario:
- Professor Pia secures funding for a large project with a brilliant overarching research idea.
- Professor Per leads a sub-project in the project focusing on an empirical investigation of the brilliant research idea. He hires PhD student Siri and Postdoc Palle to work on the experiment.
- PhD student Siri and Postdoc Palle designs and carries out the experiment.
- Administrator Anton helps with recruiting all the participants.
- PhD student Sofie provides all the sound material used in the study, and a preliminary analysis of the sound.
- Research assistant Anders helps with all the recordings for the experiment, including post-processing all the data.
- Lab engineer Erik programs the system used for data collection.
- Statistician Svein helps with the analysis of the data.
- A large part of the analysis is done using a toolbox made by Postdoc Penelope.
- Professor Pernille suggests an alternative analysis method in a seminar with a presentation of preliminary results of the data. This alternative analysis method turns out to be very promising and is therefore included in the paper.
- PhD student Siri writes the main part of the paper.
- Postdoc Palle makes all the figures and writes some of the text.
- Professor Per reads the paper and comments on a few things.
Who gets on the publication list, and in which order?
I am happy to announce a new journal article coming out of the MICRO project:
Victor E. Gonzalez-Sanchez, Agata Zelechowska and Alexander Refsum Jensenius
Correspondences Between Music and Involuntary Human Micromotion During Standstill
Front. Psychol., 07 August 2018 | https://doi.org/10.3389/fpsyg.2018.01382
Abstract: The relationships between human body motion and music have been the focus of several studies characterizing the correspondence between voluntary motion and various sound features. The study of involuntary movement to music, however, is still scarce. Insight into crucial aspects of music cognition, as well as characterization of the vestibular and sensorimotor systems could be largely improved through a description of the underlying links between music and involuntary movement. This study presents an analysis aimed at quantifying involuntary body motion of a small magnitude (micromotion) during standstill, as well as assessing the correspondences between such micromotion and different sound features of the musical stimuli: pulse clarity, amplitude, and spectral centroid. A total of 71 participants were asked to stand as still as possible for 6 min while being presented with alternating silence and music stimuli: Electronic Dance Music (EDM), Classical Indian music, and Norwegian fiddle music (Telespringar). The motion of each participant’s head was captured with a marker-based, infrared optical system. Differences in instantaneous position data were computed for each participant and the resulting time series were analyzed through cross-correlation to evaluate the delay between motion and musical features. The mean quantity of motion (QoM) was found to be highest across participants during the EDM condition. This musical genre is based on a clear pulse and rhythmic pattern, and it was also shown that pulse clarity was the metric that had the most significant effect in induced vertical motion across conditions. Correspondences were also found between motion and both brightness and loudness, providing some evidence of anticipation and reaction to the music. Overall, the proposed analysis techniques provide quantitative data and metrics on the correspondences between micromotion and music, with the EDM stimulus producing the clearest music-induced motion patterns. The analysis and results from this study are compatible with embodied music cognition and sensorimotor synchronization theories, and provide further evidence of the movement inducing effects of groove-related music features and human response to sound stimuli. Further work with larger data sets, and a wider range of stimuli, is necessary to produce conclusive findings on the subject.
I am super excited about our new Nordic Sound and Music Computing Network, which has just started up with funding from the Nordic Research Council.
This network brings together a group of internationally leading sound and music computing researchers from institutions in five Nordic countries: Aalborg University, Aalto University, KTH Royal Institute of Technology, University of Iceland, and University of Oslo. The network covers the field of sound and music from the “soft” to the “hard,” including the arts and humanities, and the social and natural sciences, as well as engineering, and involves a high level of technological competency.
At the University of Oslo we have one open PhD fellowship connected to the network, with application deadline 4 April 2018. We invite PhD proposals that focus on sound/music interaction with periodic/rhythmic human body motion (walking, running, training, etc.). The appointed candidate is expected to carry out observation studies of human body motion in real-life settings, using different types of mobile motion capture systems (full-body suit and individual trackers). Results from the analysis of these observation studies should form the basis for the development of prototype systems for using such periodic/rhythmic motion in musical interaction.
The appointed candidate will benefit from the combined expertise within the NordicSMC network, and is expected to carry out one or more short-term scientific missions to the other partners. At UiO, the candidate will be affiliated with RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion. This interdisciplinary centre focuses on rhythm as a structuring mechanism for the temporal dimensions of human life. RITMO researchers span the fields of musicology, psychology and informatics, and have access to state-of-the-art facilities in sound/video recording, motion capture, eye tracking, physiological measurements, various types of brain imaging (EEG, fMRI), and rapid prototyping and robotics laboratories.
I recently mentioned that I have been busy setting up the new MCT master’s programme. But I have been even more busy with preparing the startup of our new Centre of Excellence RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion. This is a large undertaking, and a collaboration between researchers from musicology, psychology and informatics. A visual “abstract” of the centre can be seen in the figure to the right.
Now we are recruiting lots of new people for the centre, so please apply or forward to people you think may be interested:
I am involved in a student project which uses some Arduino Mega 2560 sensor interfaces in an interactive device. It has been a while since I worked with Arduinos myself, as I am mainly working with Belas these days. Also, I have never worked with the Mega before, so I had to look around a little to figure out how to set it up with Cycling ’74’s Max.
I have previously used Maxuino for interfacing Arduinos with Max. This is a general purpose tool, with a step by step approach to connecting to the Arduino and retrieving data. This is great when it works, but due to its many options, and a somewhat convoluted patching style, I found the patch quite difficult to debug when things did not work out of the box.
I then came across the opposite to Maxuino, a minimal patch showing how to get the data right off the serial port. As can be seen from the screenshot below, it is, in fact, very simple, although not entirely intuitive if you are not into this type of thing.
One thing is the connection, another is to parse the incoming data in a meaningful way. So I decided to fork a patch made by joesanford, which had solved some of these problems in a more easy to understand patching style. For this patch to work, it requires a particular Arduino sketch (both the Max patch and Arduino sketch are available in my forked version on github). I also added a small sound engine, so that it is possible to control an additive synthesis with the sensors. The steps to make this work is explained below.
The mapping from sensor data starts by normalizing the data from the 15 analog sensors to a 0.-1. range (by dividing by 255). Since I want to control the amplitudes of each of the partials in the additive synthesis, it makes sense to slightly reduce all of the amplitudes by multiplying each element with a decreasing figure, as shown here:
Then the amplitudes are interleaved with the frequency values and sent to an ioscbank~ object to do the additive synthesis.
Not a very advanced mapping, but it works for testing the sensors and the concept.