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Schedl M.,Johannes Kepler University | Flexer A.,Austrian Research Institute for Artificial Intelligence
Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012 | Year: 2012

Personalized and context-aware music retrieval and recommendation algorithms ideally provide music that perfectly fits the individual listener in each imaginable situation and for each of her information or entertainment need. Although first steps towards such systems have recently been presented at ISMIR and similar venues, this vision is still far away from being a reality. In this paper, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking a psychological and a computer science view. Whereas the psychological point of view is mainly concerned with proper experimental design, the computer science aspect centers on modeling and machine learning problems. We further present our ideas on aspects vital to consider when elaborating user-aware music retrieval systems, and we also describe promising evaluation methodologies, since accurately evaluating personalized systems is a notably challenging task. © 2012 International Society for Music Information Retrieval. Source


Schedl M.,Johannes Kepler University | Schnitzer D.,Austrian Research Institute for Artificial Intelligence
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Current advances in music recommendation underline the importance of multimodal and user-centric approaches in order to transcend limits imposed by methods that solely use audio, web, or collaborative filtering data. We propose several hybrid music recommendation algorithms that combine information on the music content, the music context, and the user context, in particular integrating geospatial notions of similarity. To this end, we use a novel standardized data set of music listening activities inferred from microblogs (MusicMicro) and state-of-the-art techniques to extract audio features and contextual web features. The multimodal recommendation approaches are evaluated for the task of music artist recommendation. We show that traditional approaches (in particular, collaborative filtering) benefit from adding a user context component, geolocation in this case. © 2014 Springer International Publishing. Source


Flexer A.,Austrian Research Institute for Artificial Intelligence | Schnitzer D.,Johannes Kepler University
Computer Music Journal | Year: 2010

The effects of album and artist filters in audio similarity computed for very large music databases were reported. A data set D(ALL) of S = 254,398 song excerpts (30 seconds each) from a popular Web store selling music was used. The mel-frequency cepstral coefficients (MFCC) was a relatively direct representation of the spectral information of a signal and therefore of the specific sound of a song, fluctuation patterns (FP) are a more abstract kind of feature describing the amplitude modulation of the loudness per frequency band. The 30-second song excerpts in MP3 format were recomputed to 22,050 Hz mono audio signals. The frame size for computation of MFCC was 46.4 msec (1,024 samples). The resulting FP was a 12 × 30 (modulation frequencies, ranging from 0 to 10 Hz) matrix for each song. The first nearest neighbor for both methods Gaussians (G1) and FP was evaluated for every song in the database D(ALL). The first nearest neighbor was the song with minimum Kullback-Leibler divergence from the query song for method G1. Source


Gligorijevic V.,Jozef Stefan Institute | Skowron M.,Austrian Research Institute for Artificial Intelligence | Tadic B.,Jozef Stefan Institute
Physica A: Statistical Mechanics and its Applications | Year: 2013

High-resolution data of online chats are studied as a physical system in the laboratory in order to quantify collective behavior of users. Our analysis reveals strong regularities characteristic of natural systems with additional features. In particular, we find self-organized dynamics with long-range correlations in user actions and persistent associations among users that have the properties of a social network. Furthermore, the evolution of the graph and its architecture with specific k-core structure are shown to be related with the type and the emotion arousal of exchanged messages. Partitioning of the graph by deletion of the links which carry high arousal messages exhibits critical fluctuations at the percolation threshold. © 2012 Elsevier B.V. All rights reserved. Source


Holighaus N.,Austrian Academy of Sciences | Dorfler M.,University of Vienna | Velasco G.A.,University of the Philippines at Diliman | Grill T.,Austrian Research Institute for Artificial Intelligence
IEEE Transactions on Audio, Speech and Language Processing | Year: 2013

Audio signal processing frequently requires time-frequency representations and in many applications, a non-linear spacing of frequency bands is preferable. This paper introduces a framework for efficient implementation of invertible signal transforms allowing for non-uniform frequency resolution. Non-uniformity in frequency is realized by applying nonstationary Gabor frames with adaptivity in the frequency domain. The realization of a perfectly invertible constant-Q transform is described in detail. To achieve real-time processing, independent of signal length, slice-wise processing of the full input signal is proposed and referred to as sliCQ transform. By applying frame theory and FFT-based processing, the presented approach overcomes computational inefficiency and lack of invertibility of classical constant-Q transform implementations. Numerical simulations evaluate the efficiency of the proposed algorithm and the method's applicability is illustrated by experiments on real-life audio signals. © 2006-2012 IEEE. Source

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