This paper introduces an open research platform for exploring haptic interactions with co-located, capacitive and piezoresistive sensors. The solution uses readily available material, hardware and software components and allows for experiments on many system levels from low-level material concerns up to high-level sensor fusion software. This provides the HCI community with a platform to accelerate explorations of the many applications that have opened up of sensor fusion in haptic interaction.

The design of an unusually simple fabric-based touch andpressure sensor is introduced. An analysis of the raw sensor data is shown to have significant non-linearities and non-uniform noise. Using support vector machine learning and a state-dependent adaptive filter it is demonstratedt that these problems can be overcome. The method is evaluated quantitatively using a statistical estimate of the instantaneous rate of information transfer. The SVMregression alone is shown to improve the gesture signalinformation rate by up to 20% with zero added latency, andin combination with filtering by 40% subject to a constant latency bound of 10 milliseconds.

We address the problem of mining musical specifications from a training set of songs and using these specifications in a machine improvisation system capable of generating improvisations imitating a given style of music. Our inspiration comes from control improvisation, which combines learning and synthesis from formal specifications. We mine specifications from symbolic musical data with musical and general usage patterns. We use the mined specifications to ensure that an improvised musical sequence satisfies desirable properties given a harmonic context and phrase structure. We present a specification mining strategy based on pattern graphs and apply it to the problem of supervising the improvisation of blues songs. We present an analysis of the mined specifications and compare the results of improvisations generated with and without specifications.