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The contribution of this paper is threefold: First, we propose modifications to Fluctuation Patterns [14]. The resulting descriptors are evaluated in the task of rhythm similarity computation on the " Ballroom Dancers " collection. Second, we show that by combining these rhythmic de-scriptors with a timbral component, results for rhythm similarity(More)
'Hubness' has recently been identified as a general problem of high dimensional data spaces, manifesting itself in the emergence of objects, so-called hubs, which tend to be among the k nearest neighbors of a large number of data items. As a consequence many nearest neighbor relations in the distance space are asymmetric, that is, object y is amongst the(More)
There are many MIR applications for which we would like to be able to determine the perceived tempo of a song automatically. However, automatic tempo extraction itself is still an open problem. In general there are two tempo extraction methods, either based on the estimation of inter-onset intervals or based on self similarity computations. To predict a(More)
We present a filter-and-refine method to speed up acoustic audio similarity queries which use the Kullback-Leibler divergence as similarity measure. The proposed method rescales the divergence and uses a modified FastMap [1] implementation to accelerate nearest-neighbor queries. The search for similar music pieces is accelerated by a factor of 10−30(More)
This thesis develops a large-scale music recommendation system. To achieve this goal we solve three problems preventing the currently top-performing class of content-based music similarity algorithms from being used as recommendation engine in huge databases with millions of songs: First, we show how to correctly use the non-vectorial music similarity(More)
A new algorithm for automatic generation of playlists with an inherent sequential order is presented. Based on a start and end song it creates a smooth transition allowing users to discover new songs in a music collection. The approach is based on audio similarity and does not require any kind of meta data. It is evaluated using both objective genre labels(More)
We use results from the 2011 MIREX " Audio Music Similarity and Retrieval " task for a meta analysis of the hub phenomenon. Hub songs appear similar to an undesirably high number of other songs due to a problem of measuring distances in high dimensional spaces. Comparing 17 algorithms we are able to confirm that different algorithms produce very different(More)
This work introduces Mutual Proximity, an unsupervised method which transforms arbitrary distances to similarities computed from the shared neighborhood of two data points. This reinterpretation aims to correct inconsistencies in the original distance space, like the hub phenomenon. Hubs are objects which appear unwontedly often as nearest neighbors in(More)
We present an approach that offers the user a convenient and meaningful way to access her music on a mobile device. By exploiting information on acoustic similarity and community-based music labels, a music collection is automatically structured and described to allow for easy orientation and navigation within the collection. To this end, the complete(More)