I have been doing several long recordings with GoPro cameras recently. The cameras automatically split the recordings into 4GB files, which leaves me with a myriad of files to work with. I have therefore made a script to help with the pre-processing of the files.
This is somewhat similar to the script I made to convert MXF files to MP4, but with better handling of the temp file for storing information about the files to merge:
Save the script above as mergevideos.sh, put it in the folder of your files, make it executable, with a command like:
chmod u+x mergevideos.sh
run the file:
and watch the magic.
The script above can be remixed in various ways. For example, if you want a smaller output file (the original GoPro files are quite large), you can use FFmpeg’s default MP4 compression settings by removing the “-c copy” part in the last line above. That will also make the script take much longer, since it will recompress the output file.
What are the digital competencies needed in the future? Our head of department has challenged me to talk about this topic at an internal seminar today. Here is a summary of what I said.
Competencies vs skills
First, I think it is crucial to separate competencies from skills. The latter relates to how you do something. There has been much focus on teaching skills, mainly teaching people how to use various software or hardware. This is not necessarily bad, but it is not the most productive thing in higher education, in my opinion. Developing competency goes beyond learning new skills.
Some argue that skill is only one of three parts of competency, with knowledge and abilities being the others:
Skills + Knowledge + Abilities = Competencies
So a skill can be seen as part of competency, but it is not the same. This is particularly important in higher education, where the aim is to train students for life-long careers. As university teachers, we need to develop our students’ competencies, not only their skills.
Digital vs technological competency
Another misunderstanding is that “digital” and “technology” are synonyms, and they are not. Technologies can be either digital or analogue (or a combination). Think of “computers”. The word originated from humans (often women) that manually computed advanced calculations. Human computers were eventually replaced by mechanical machine computers, while today we mainly find digital computers. Interestingly, there is a growing amount of research on analogue computers again.
I often argue that traditional music notation is a digital representation. Notes such as “C”, “D”, and “E” are symbolic representations of a discrete nature, and these digital notes may be transformed into analogue tones once performed.
One often talks about the differences between acoustic and digital instruments. This is a division I criticise in my upcoming book, but I will leave that argument aside for now. Independent of the sound production, I have over the years grown increasingly fond of Tellef Kvifte’s approach to separating between analogue and digital control mechanisms of musical instruments. Then one could argue that an acoustic piano is a digital instrument because it is based on discrete control (with separate keys for “C”, “D”, “E”…).
Four levels of technology research and usage
When it comes to music technologies, I often like to think of four different layers: basic research, applied research and development, usage, and various types of meta-perspectives. I have given some examples of what these may entail in the table below.
Applied research and development
Music theory Music cognition Musical interaction …
Instrument making Composing Producing Performing Analysing …
PedagogyPsychology Sociology History Aesthetics …
Digital representation Signal processing Machine learning …
Searching Writing Illustrating …
Four layers of (music) technology research and usage.
Most of our research activities can be categorised as being on the basic research side (plus various types of applied R&D, although mainly at a prototyping stage) or on the meta-perspectives side. To generalise, one could say that the former is more “technology-oriented” while the latter is more “humanities-oriented.” That is a simplification of a complex reality, but it may suffice for now.
The problem is that many educational activities (ours and others) focus on the use of technologies. However, today’s kids don’t need to learn how to use technologies. Most agree that they are eager technology users from the start. It is much more critical that they learn more fundamental issues related to digitalisation and why technologies work the way they do.
Given the level of digitisation that has happened around us over the last decades, I am often struck by the lack of understanding of digital representation. By that, I mean a fundamental understanding of what a digital file contains and how its content ended up in a digital form. This also influences what can be done to the content. Two general examples:
Text: even though the content may appear somewhat identical for those looking at a .TXT file versus a .DOCX/ODT file, these are two completely different ways of representating textual information.
Numbers: storing numbers in a .DOCX/ODT table is completely different from storing the same numbers in a .XLSX/ODS file (or a .CSV file for that matter).
One can think about these as different file formats that one can convert between. But the underlying question is about what type of digital representation one wants to capture and preserve, which also influences what you can do to the content.
From a musical perspective, there are many types of digital representations:
Scores: MIDI, notation formats, MusicXML
Audio: uncompressed vs. compressed formats, audio descriptor formats
Video: uncompressed vs. compressed formats, video descriptor formats
Students (and everyone else) need to understand what such digital representations mean and what they can be used for.
Computers are based on algorithms, a well-defined set of instructions for doing something. Algorithms can be written in computer code, but they can also be written with a pen on paper or drawn in a flow diagram. The main point is that algorithmic thinking is a particular type of reasoning that people need to learn. It is essential to understand that any complex problem can be broken down into smaller pieces that can be solved independently.
Not everyone will become programmers or software engineers, but there is an increased understanding that everyone should learn basic coding. Then algorithmic thinking is at the core. At UiO, this has been implemented widely in the Faculty for Mathematics and Natural Sciences through the Computing in Science Education. We don’t have a similar initiative in the Faculty of Humanities, but several departments have increased the number of courses that teach such perspectives.
There is a lot of buzz around AI, but most people don’t understand what it is all about. As I have written about several times on this blog (here and here), this makes people either overly enthusiastic or sceptical about the possibilities of AI. Not everyone can become an AI expert, but more people need to understand AI’s possibilities and limitations. We tried to explain that in the “AI vs Ary” project, as documented in this short documentary (Norwegian only):
The future is analogue
In all the discussions about digitisation and digital competency, I find it essential to remind people that the future is analogue. Humans are analogue; nature is analogue. We have a growing number of machines based on digital logic, but these machines contain many analogue components (such as the mechanical keys that I am typing this text on). Much of the current development in AI is bio-inspired, and there are even examples of new analogue computers. Understanding the limitations of digital technologies is also a competency that we need to teach our students.
All in all, I am optimistic about the future. There is a much broader understanding of the importance of digital competency these days. Still, we need to explain that this entails much more than learning how to use particular software or hardware devices. It is OK to learn such skills, but it is even more important to develop knowledge about how and why such technologies work in the first place.
The MICRO project sought to investigate the close relationships between musical sound and human bodily micromotion. Micromotion is here used to describe the smallest motion that we can produce and experience, typically at a rate lower than 10 mm/s.
The last decades have seen an increased focus on the role of the human body in both the performance and the perception of music. Up to now, however, the micro-level of these experiences has received little attention.
The main objective of MICRO was broken down into three secondary objectives:
Define a set of sub-categories of music-related micromotion.
Understand more about how musical sound influences the micromotion of perceivers and which musical features (such as melody, harmony, rhythm, timbre, loudness, spatialization) come into play.
Develop conceptual models for controlling sound through micromotion, and develop prototypes of interactive music systems based on these models.
The project completed most of its planned activities and several more:
The scientific results include many insights about human music-related micromotion. Results have been presented in one doctoral dissertation, two master theses, several journal papers, and at numerous conferences. As hypothesized, music influences human micromotion. This has been verified with different types of music in all the collected datasets. We have also found that music with a regular and strong beat, particularly electronic dance music, leads to more motion. Our data also supports the idea that music with a pulse of around 120 beats per minute is more motion-inducing than music with slower or faster tempi. In addition, we found that people generally moved more when listening with headphones. Towards the end of the project, we began studying whether there are individual differences. One study found that people who score high on empathic concern move more to music than others. This aligns with findings from recent studies of larger-scale music-related body motion.
I am very happy about the outcomes of the MICRO project. This is largely thanks to the fantastic team, particularly postdoctoral fellow Victor Gonzalez Sanchez and doctoral fellow Agata Zelechowska.
Results from the Sverm project inspired the MICRO project, and many lines of thought will continue in my new AMBIENT project. I am looking forward to researching unconscious and involuntary micromotion in the years to come.
I am recording a lot of short videos these days for my sound actions project. Sometimes the recordings end up being rotated, which is based on the orientation sensor (probably the gyroscope) of my mobile phone. This rotation is not part of the recorded video data, it is just information written into the header of the MPEG file. That also means that it is possible to change the rotation without recoding the file. It is possible to see the rotation by looking at the metadata of a file:
ffmpeg -i filename.mp4
Then you will see a lot of information about the file. A bit down in the list is information about the rotation:
Side data: displaymatrix: rotation of -90.00 degrees
Fixing it is as simple as running this command on the file:
I am happy to announce that I am recruiting for my new research project AMBIENT: Bodily Entrainment to Audiovisual Rhythms. The project will continue my line of research into the effects of sound and visuals on our bodies and minds and the creative use of such effects. Here is a short video in which I explain the motivation for the project:
The idea is to put together a multidisciplinary team of three early career researchers experienced with one or more of the following methods: sound analysis, video analysis, interviews, questionnaires, motion capture, physiological sensing, statistics, signal processing, machine learning, interactive (sound/music) systems. The announcement texts are available here:
Application deadline: 15 March 2022. Do not hesitate to get in touch if you have any questions about the positions.
About the project
Much focus has been devoted to understanding the “foreground” of human activities: things we say, actions we do, sounds we hear. AMBIENT will study the sonic and visual “background” of indoor environments: the sound of a ventilation system in an office, the footsteps of people in a corridor, or people’s fidgeting in a classroom.
The project aims to study how such elements influence people’s bodily behaviour and how people feel about the rhythms in an environment. This will be done by studying how different auditory and visual stimuli combine to create rhythms in various settings.
The hypothesis is that various types of rhythms influence people’s bodily behaviour through principles of entrainment, that is, the process by which independent rhythmical systems interact with each other.
The primary objective of AMBIENT is to understand more about bodily entrainment to audiovisual rhythms in both local and telematic environments. This will be studied within everyday workspaces like offices and classrooms.
The primary objective can be broken down into three secondary objectives:
Understand more about the rhythms of in-door environments, and make a theoretical model of such rhythms that can be implemented in software.
Understand more about how people interact with the rhythms of in-door environments, both when working alone – and together.
Explore how such rhythms can be captured and (re)created in a different environment using state-of-the-art audiovisual technologies.
The work in AMBIENT is divided into five work packages:
WP1: Theoretical Development
WP2: Observation study of individuals in their offices
WP3: Observation study of physical-virtual workspaces
WP4: Exploration of (re)creation of ambience in a telematic classroom
WP5: Software development
The work packages overlap and feed into each other in various ways.
The AMBIENT project is an open research lighthouse project. The aim is to keep the entire research as open as possible, including sharing methods, data, publications, etc.