Dominik Schnitzer

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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)
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 descriptors with a timbral component, results for rhythm similarity computation(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)
We investigate an approach to a music search engine that indexes music pieces based on relatedWeb documents. This allows for searching for relevant music pieces by issuing descriptive textual queries. In this paper, we examine the effects of incorporating audio-based similarity into the text-based ranking process – either by directly modifying the retrieval(More)
We propose a new approach to a music search engine that can be accessed via natural language queries. As with existing approaches, we try to gather as much contextual information as possible for individual pieces in a (possibly large) music collection by means of Web retrieval. While existing approaches use this textual information to construct(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)
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)