MobileHCI Talks – Wednesday 14th August, SAWB 422
Daryl Weir – Sparse Selection of Training Data for Touch Correction Systems
Techniques to improve the accuracy of mobile touch screen devices typically require the use of a large number of training points. In this paper, we describe a method for selecting train- ing points such that high performance can be attained with fewer data. We use the Relevance Vector Machine (RVM) algorithm, and show that performance improvements can be obtained with fewer than 10 training examples. We show that the distribution of training points is conserved across users and contains interesting structure, and compare the RVM to two other offset prediction models for small training set sizes.
Daniel Boland – Finding My Beat: Personalised Rhythmic Filtering for Mobile Music Interaction
A novel interaction style is presented, allowing in-pocket music selection by tapping a song’s rhythm on a device’s touchscreen or body. We introduce the use of rhythmic queries for music retrieval, employing a trained generative model to improve query recognition. We identify rhythm as a fundamental feature of music which can be reproduced easily by listeners, making it an effective and simple interaction technique for retrieving music. We observe that users vary in which instruments they entrain with and our work is the first to model such variability. An experiment was performed, showing that after training the generative model, retrieval performance improved two-fold. All rhythmic queries returned a highly ranked result with the trained generative model, compared with 47% using existing methods. We conclude that generative models of subjective user queries can yield significant performance gains for music retrieval and enable novel interaction techniques such as rhythmic filtering.