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Arthur Flexer
Researcher at Johannes Kepler University of Linz
Publications - 93
Citations - 2340
Arthur Flexer is an academic researcher from Johannes Kepler University of Linz. The author has contributed to research in topics: Similarity (network science) & Music information retrieval. The author has an hindex of 23, co-authored 89 publications receiving 2168 citations. Previous affiliations of Arthur Flexer include University of Vienna & Austrian Research Institute for Artificial Intelligence.
Papers
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Proceedings Article
Improvements of Audio-Based Music Similarity and Genre Classificaton.
TL;DR: It is shown that spectral similarity with complementary information from fluctuation patterns including two new descriptors derived from them can be combined to form genre classification patterns.
Book ChapterDOI
On the Use of Self-Organizing Maps for Clustering and Visualization
TL;DR: It is demonstrated that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization, and it is shown that this flexibility comes with a price in terms of impaired performance.
Journal ArticleDOI
A reliable probabilistic sleep stager based on a single EEG signal
TL;DR: A probabilistic continuous sleep stager based on Hidden Markov models using only a single EEG signal that detects the cornerstones of human sleep (wakefulness, deep and REM sleep) with around 80% accuracy based on data from asingle EEG channel.
Journal ArticleDOI
On the use of self-organizing maps for clustering and visualization
TL;DR: It is shown that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization, and it is made obvious that this flexibility comes with a price in terms of impaired performance.
Journal ArticleDOI
The neglected user in music information retrieval research
TL;DR: This article investigates and discusses literature on the topic of user-centric music retrieval and reflects on why the breakthrough in this field has not been achieved yet, and presents ideas on aspects crucial to consider when elaborating user-aware music retrieval systems.