Züri Fäscht 2013
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Recent technologies provide a vast amount of new possibilities and developments in the field of music. But one area is nearly untouched so far: Musical instruments.
Although it might sound a little bit unromantic, playing and exercising a musical instrument and making music is a difficult task, similar to training in sports. In the latter, technology based training assistance is already common. To rise supportive technologies for musical instruments, several projects and experiments are carried out in our laboratories. With several technologies measurements are conducted to receive objective and individual data of musicians and live performances. With these, investigations regarding e.g. coordination, fatigue and general performance monitoring is possible.
Beside this, new interfaces, especially based on sensor technologies are developed and evaluated. This could lead to new musical interfaces and feedback methods or to augmented traditional musical instruments.(Contact and recent publications click here).
Some informal information about our music project is available here in a video of DRS3.
From a technical point of view, instrumental music making involves mainly audible and visible playing parameters. Parameters like force, pressure and fast movements, happening within milliseconds are particularly much more difficult to capture. Our research is focusing on these parameters additional to movement and audio analysis. To achieve this, several sensors are integrated unobtrusively into and fixed on the musical instrument. The evaluation of these new sensor setups is done with amateur musicians, students of the ZHDK and professionals.
A further goal of the use of sensors is a more simple integration of conventional musical instruments into electronic music environments by converting the data to MIDI and OSC.
Exercising is a daily routine in learning a musical instrument. It is one of the most important tasks but very little attention is drawn to it. But there is a high potential to increase the efficiency and to learn faster. Beside many others, this could be obtained/reached with new sensor and feedback technologies.
One example is the detection of fatigue of the musician while exercising.
Fatigue in daily instrumental exercising is a common encountered problem.
Musicians practice overly long and pause concepts are underrepresented resulting into performance shortcomings and work-related injuries. For example violinists need to hold their instrument while playing in an ergonomically disadvantageous position that induces fatigue.
We attempted to uncover fatigue-related motion and movement changes of the musician based on sensor measurements. The sensor data are recorded and specific movement patterns are recognized. Results show that features associated to individual fatigue during musical instrument playing can be uncovered, potentially helping to prevent injuries and to optimize practicing.
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