<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmeder, Andrew</style></author><author><style face="normal" font="default" size="100%">Freed, Adrian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Support Vector Machine Learning for Gesture Signal Estimation with a Piezo Resistive Fabric Touch Surface</style></title><secondary-title><style face="normal" font="default" size="100%">NIME</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><abstract><style face="normal" font="default" size="100%">The design of an unusually simple fabric-based touch and pressure 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 demonstrated that these problems can be overcome. The method is evaluated quantitatively using a statistical estimate of the instantaneous rate of information transfer. The SVM regression alone is shown to improve the gesture signal information rate by up to 20% with zero added latency, and in combination with filtering by 40% subject to a constant latency bound of 10 milliseconds.</style></abstract><notes><style face="normal" font="default" size="100%">Draft paper in submission. Do not redistribute without permission.</style></notes></record></records></xml>
