2 research outputs found
A cluster analysis of harmony in the McGill Billboard dataset
We set out to perform a cluster analysis of harmonic structures (specifically, chord-to-chord transitions) in the McGill Billboard dataset, to determine whether there is evidence of multiple harmonic grammars and practices in the corpus, and if so, what the optimal division of songs, according to those harmonic grammars, is. We define optimal as providing meaningful, specific information about the harmonic practices of songs in the cluster, but being general enough to be used as a guide to songwriting and predictive listening. We test two hypotheses in our cluster analysis — first that 5–9 clusters would be optimal, based on the work of Walter Everett (2004), and second that 15 clusters would be optimal, based on a set of user-generated genre tags reported by Hendrik Schreiber (2015). We subjected the harmonic structures for each song in the corpus to a K-means cluster analysis. We conclude that the optimal clustering solution is likely to be within the 5–8 cluster range. We also propose that a map of cluster types emerging as the number of clusters increases from one to eight constitutes a greater aid to our understanding of how various harmonic practices, styles, and sub-styles comprise the McGill Billboard dataset
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Detecting User Frustration From Smartphone Sensors: A Multimodal Classification Approach
Most smartphone applications are unaware that users feel frustrated by a bug, an error, or a usability problem. Could a smartphone be “smart” enough to detect that its user became frustrated by something? A pilot study was conducted as proof of concept, showing that sensor readings collected in the background from a smartphone could be used to detect user frustration after the onset of a frustrating event due to a bug or a usability problem. Twenty-one participants were asked to perform a series of multitasking tasks, during which errors were purposely introduced to frustrate them. Sensor data, including motion, touch, and camera, were collected and used to train a binary classifier that is able to detect frustration with reasonable accuracy when merging data from different modalities (motion sensors and touch gestures)