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In the course "Acoustic Interfaces" we wrote a C++ Machine-Learning library based on a K-Clustering Algorithm. It's purpose is to classify Microphone Inputs into different frequency based classes. After this cluster (training) process the algorithm is able to classify similiar microphone input in realtime. The algorithm is running on a teensy 4.0 with an audio shield. | In the course "Acoustic Interfaces" we wrote a C++ Machine-Learning library based on a K-Clustering Algorithm. It's purpose is to classify Microphone Inputs into different frequency based classes. After this cluster (training) process the algorithm is able to classify similiar microphone input in realtime. The algorithm is running on a teensy 4.0 with an audio shield. | ||
I have to say, that it was quite hard for me to come up with an idea for the creative, musical use of this technique. As i dont wanted to implement this algorithm for the | I have to say, that it was quite hard for me to come up with an idea for the creative, musical use of this technique. As i dont wanted to implement this algorithm for the classic purpose of sound detection issues, i came up with a more abstract idea of use. | ||
My idea was a intention based gesture/timbre detection through the piezo microphone input. This input is expressed through Midi Notes and muscial scales. For example the aggresive touch on the interface surface should express in a, somehow, corresponding sequence of notes (or melody). | My idea was a intention based gesture/timbre detection through the piezo microphone input. This input is expressed through Midi Notes and muscial scales. For example the aggresive touch on the interface surface should express in a, somehow, corresponding sequence of notes (or melody). | ||