Interactive Machine Learning for Music

As illustrated in the videos above, ML can make it much easier to work with high-dimensional and/or noisy signals, such as those that come from sensors, audio, and video. Making good mappings for From the Waters, MARtLET, or Spring Spyre through writing programming code would be incredibly difficult and time consuming, and perhaps impossible. Supervised learning algorithms are designed to be able to infer functional relationships between inputs and outputs in a training set, even in the presence of noise, and they can often do this faster and more accurately than a person.