Bea Vad presents at ISMIR 2015 in Spain

Bea at ISMIR

Bea presented the paper:
B. Vad, Boland, D., Williamson, J., Murray-Smith, R., and Steffensen, P. B., Design and evaluation of a probabilistic music projection interface, In: 16th International Society for Music Information Retrieval Conference, Malaga, Spain, 26-30 Oct 2015.

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A conceptual model of the future of input devices

Speaker: John Williamson
Date: 14 October, 2015
Time: 14:00 – 15:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Turning sensor engineering into advances into human computer interaction is slow, ad hoc and unsystematic. I’ll discuss a fundamental approach to input device engineering, and illustrate how machine learning could have the exponentially-accelerating impact in HCI that it has had in other fields.

 

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Seminar: Engaging with Music Retrieval

Engaging with Music Retrieval

Speaker: Daniel Boland
Date: 09 September, 2015
Time: 14:00 – 15:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Music collections available to listeners have grown at a dramatic pace, now spanning tens of millions of tracks. Interacting with a music retrieval system can thus be overwhelming, with users offered ‘too-much-choice’. The level of engagement required for such retrieval interactions can be inappropriate, such as in mobile or multitasking contexts. Using listening histories and work from music psychology, a set of engagement-stratified profiles of listening behaviour are developed. The challenge of designing music retrieval for different levels of user engagement is explored with a system allowing users to denote their level of engagement and thus the specificity of their music queries. The resulting interaction has since been adopted as a component in a commercial music system.

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Deep non-parametric learning with Gaussian processes Speaker: Andreas Damianou, Sheffield University

This talk will discuss deep Gaussian process models, a recent approach to combining deep probabilistic structures with Bayesian nonparametrics. The obtained deep belief networks are constructed using continuous variables connected with Gaussian process mappings; therefore, the methodology used for training and inference deviates from traditional deep learning paradigms. The first part of the talk will thus outline the associated computational tools, revolving around variational inference. In the second part, we will discuss models obtained as special cases of the deep Gaussian process, namely dynamical / multi-view / dimensionality reduction models and nonparametric autoencoders. The above concepts and algorithms will be demonstrated with examples from computer vision (e.g. high-dimensional video, images) and robotics (motion capture data, humanoid robotics).

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