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Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the(More)
Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily(More)
We present a novel, innovative user interface to music repositories. Given an arbitrary collection of digital music files, our system creates a virtual landscape which allows the user to freely navigate in this collection. This is accomplished by automatically extracting features from the audio signal and training a Self-Organizing Map (SOM) on them to form(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 technique for combining audio signal-based music similarity with web-based musical artist similarity to accelerate the task of automatic playlist generation. We demonstrate the applicability of our proposed method by extending a recently published interface for music players that benefits from intelligent structuring of audio collections. While(More)
Today, among the best-performing audio-based music similarity measures are algorithms based on Mel Frequency Cepstrum Coefficients (MFCCs). In these algorithms, each music track is modelled as a Gaussian Mixture Model (GMM) of MFCCs. The similarity between two tracks is computed by comparing their GMMs. One drawback of this approach is that the distance(More)
An approach is presented to automatically build a search engine for large-scale music collections that can be queried through natural language. While existing approaches depend on explicit manual annotations and meta-data assigned to the individual audio pieces, we automatically derive descriptions by making use of methods from Web Retrieval and Music(More)
In this paper, we present a similarity measure for music artists based on search results of Google queries. Co-occurrences of artist names on web pages are analyzed to measure how often two artists are mentioned together on the same web page. We estimate conditional probabilities using the extracted page count. These conditional probabilities give a(More)
Abstract We explore a simple, web-based method for predicting the genre of a given artist based on co-occurrence analysis, i.e. analyzing co-occurrences of artist and genre names on music-related web pages. To this end, we use the page counts provided by Google to estimate the relatedness of an arbitrary artist to each of a set of genres. We investigate(More)
music repositories called nepTune creates a virtual landscape for an arbitrary collection of digital music files, letting users freely navigate the collection. Automatically extracting features from the audio signal and clustering the music pieces accomplish this. The clustering helps generate a 3D island landscape. T he ubiquity of digital music is a(More)