Discrete Chasm

frequency modulation virtual synthesizer
perform w/ Leap Motion & TouchOSC

Sound Image

The musical production process carries many dynamics starting from the mental process, such as the environment in which the sound is produced, the materials used and the ways and purposes of using these materials. In this context, the method I used in my project is to directly incorporate the data I obtained with "Leap Motion", the sensor that captures certain movements I make with my hands, directly into the sound synthesis process. In the context of gesture control, I refer to this approach as "Direct Acquisition" because of the way movements are captured and used (direct involvement in the synthesis process of the data obtained using sensors)(Gibet & Marteau, 2004:634). In this sense, the harmony between the movement of the hands in real space and the integration process that will correspond to this in the sound synthesis process is a very important point. In order to include the real- world equivalent of the movement in the sound synthesis process on a metaphorical plane, I also placed the speed, direction and realization time of the movements within a certain range for each parameter. However, the "expressivity" of the movement mechanism here is still not fully settled. Expressivity of the interfaces rely on the goals of the user and the context of output perception to generate information (Malloch et al., 2006:50). It is almost impossible to place the movements on a meaningful ground and find the musical equivalent of them. At this point, it would be appropriate to try to converge to each other by considering each phenomenon in terms of its aesthetics. In this sense, being aware of the natural physical process underlying motion, while providing insight into the choice of musical material, does not necessarily provide an obvious musical solution (Winkler, 1995).

Sound Image

routing strategies for discrete chasm

Sound Image

Live performance of discrete chasm

You can discover the atmosphere which this maxMSP patch provided. It works with several sound sources, modulators and also machine learning tools which developed by FluCoMa team. You can find detailed information about the sounds and the notion of project from the paper added below. You can freely download and check the idea in it. Do not forget to citation issues in case you use in your papers. Thanks.