Adaptive Multimedia Retrieval. Identifying, Summarizing, and by Matthias Geier, Sascha Spors, Stefan Weinzierl (auth.),
By Matthias Geier, Sascha Spors, Stefan Weinzierl (auth.), Marcin Detyniecki, Ulrich Leiner, Andreas Nürnberger (eds.)
This quantity constitutes the refereed lawsuits of the sixth foreign Workshop on Adaptive Multimedia Retrieval, AMR 2008, held in Berlin, Germany, in June 2008.
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Additional resources for Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music: 6th International Workshop, AMR 2008, Berlin, Germany, June 26-27, 2008. Revised Selected Papers
An atomic condition is one of the following alternatives: 10 This does not mean a restriction for retrieval queries based on different search terms since terms are there expressed by different dimensions of a chosen vector space model. 36 D. Zellh¨ofer and I. Schmitt ∧ θ1 c1 ∧ θ2 θ3 c2 θ4 c3 ∨ c4 c1 ¬θ1 ∨ c2 ∨ ¬θ2 c3 ∨ ¬θ3 c4 ¬θ4 Fig. 4. Weight substitution in CQQL 1. A select condition ‘A = c’ on an attribute A and a constant c is an atom. 2. An equality condition ‘Ai1 = . . = Aik ’ on k attributes of the same type is an atom.
Low-level features describe abstract characteristics of single frames of an audio signal. The implemented features are spectral centroid, spectral ﬂatness, spectral crest factor, audio spectrum envelope, Mel-frequency cepstral coeﬃcients, loudness, and zero crossing rate. A detailed description can be found in . Mid-level features are designed to describe abstract characteristics of the entire audio signal or segments. A straightforward approach to form mid-level features is computing the mean and standard deviation of time series of low-level features.
1 Concept System Overview An overview of the system components is given in Fig. 1. The recommendation engine (RE) creates a playlist sorted by the distance to a given song. This song is speciﬁed by the user and will be denoted as seed song. The user feedback system (UFB system) analyzes the user’s relevance feedback about already played songs and adapts the playlist accordingly. Audio features are extracted from the audio signal to capture relevant information about the musical characteristics.