A Systematic Comparison of Music Similarity Adaptation Approaches

In order to support individual user perspectives and different retrieval tasks, music similarity can no longer be considered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet distance weighting on the MagnaTagATune dataset. A crossvalidation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special attention is paid to the amount of training data necessary for making similarity predictions on unknown data, the number of model parameters and the amount of information available about the music itself. 1

An Approach towards Automated Fault Interpretations in Seismic Data

In this paper, we present a methodology for interpreting faults from three dimensional seismic data. Faults are individual fractures across which there are visible offsets of horizons (or rock layers). 3D seismic data - images of subsurface structure generated by reflecting seismic waves off rock layers - have been used for hypothesizing subsurface structures. Since interpretation of seismic data is a highly time-consuming task, automated tools to assist the interpretation are crucial. Our work focuses on automating the correlation of horizons across a fault so that helping in defining the fault's geometry