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 clusters of similar sounding pieces of music. Subsequently, a Smoothed Data Histogram (SDH) is calculated on the SOM and interpreted as a three-dimensional height profile. This height profile is visualized as a three-dimensional island landscape containing the pieces of music. While moving through the terrain, the closest sounds with respect to the listener’s current position can be heard. This is realized by anisotropic auralization using a 5.1 surround sound model. Additionally, we incorporate knowledge extracted automatically from the web to enrich the landscape with semantic information. More precisely, we display words and related images that describe the heard music on the landscape to support the exploration.

MusicSun is a graphical user interface to discover artists. Artists are recommended based on one or more artists selected by the user. The recommendations are computed by combining 3 different aspects of similarity. The users can change the impact of each of these aspects. In addition words are displayed which describe the artists selected by the user. The user can select one of these words to focus the search on a specific direction. In this paper we present the techniques used to compute the recommendations and the graphical user interface. Furthermore, we present the results of an evaluation with 33 users. We asked them, for example, to judge the usefulness of the different interface components and the quality of the recommendations. 1

How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. For example, cultural information as well constitute how we experience music. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, for instance, can be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents – many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use ‘slang ’ language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word / verse length, and the amount of repeating text. In this paper, we present a novel set of features developed from textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classification on a test collection in order to demonstrate our analysis. 1

We present a new way of accessing large sets of musical artists based on high-level concepts. The concepts are derived and assigned to individual artists by an automatic procedure: Using a list of music-related words and phrases, the well-known TF×IDF approach is applied to analyse the 100 top web pages related to each artist, as delivered by a web search engine. This data then is decomposed into a number of “archetypical ” bases or “concepts ” by Non-Negative Matrix Factorisation (NMF). Each artist is then described by the amount by which it is related to each of these concepts. In our browser application presented here, such a representation allows for independently adjusting the weight of each of these concepts, to recommend those artists that best match the desired query profile. 1.

Abstract not found

Abstract. In this paper, we present a technique to automatically create music maps labeled with semantic descriptors, the so called Music Description Maps (MDM). Based on a Self-organizing Map (SOM) trained on audio features, we create term profiles that characterize the type of music in the various clusters. To this end, we efficiently retrieve musicrelated term descriptors for music artists from the Web. These descriptors are used in conjuction with a SOM-labeling strategy to identify words and phrases commonly used in the context of the associated music. Additionally, regions of similar clusters are uncovered. Music maps labeled in such a manner can aid the user in retrieving desired music from a very large repository, either by providing landmarks on the map or by allowing the formulation of queries consisting of terms describing the musical content. 1

In many areas multimedia technology has made its way into mainstream. In the case of digital audio this is manifested in numerous online music stores having turned into profitable businesses. The widespread user adaption of digital audio both on home computers and mobile players show the size of this market. Thus, ways to automatically process and handle the growing size of private and commercial collections become increasingly important; along goes a need to make music interpretable by computers. The most obvious representation of audio files is their sound – there are, however, more ways of describing a song, for instance its lyrics, which describe songs in terms of content words. Lyrics of music may be orthogonal to its sound, and differ greatly from other texts regarding their (rhyme) structure. Consequently, the exploitation of these properties has potential for typical music information retrieval tasks such as musical genre classification; so far, there is a lack of means to efficiently combine these modalities. In this paper, we present findings from investigating advanced lyrics features such as the frequency of certain rhyme patterns, several parts-of-speech features, and statistic features such as words per minute (WPM). We further analyse in how far a combination of these features with existing acoustic feature sets can be exploited for genre classification and provide experiments on two test collections.

Research in intelligent music processing is experiencing an enormous boost these days due to the emergence of the new application and research field of Music Information Retrieval (MIR). The rapid growth of digital music collections and the concomitant shift of the music market towards digital music distribution urgently call for intelligent computational support

Abstract. This review article presents the state-of-the-art in contextbased music similarity estimation. It gives an overview of different sources of context-based data on music entities and summarizes various approaches for constructing similarity measures based on the collaborative or cultural knowledge that is incorporated in these data sources. The strength of such context-based measures is elaborated as well as their drawbacks discussed. 1

Microblogging services, such as Twitter, have risen enormously in popularity during the past years. Despite their popularity, such services have never been analyzed for MIR purposes, to the best of our knowledge. We hence present first investigations of the usability of music artist-related microblogging posts to perform artist labeling and similarity estimation tasks. To this end, we look into different text-based indexing models and term weighting measures. Two artist collections are used for evaluation, and the different methods are evaluated against data from last.fm. We show that microblogging posts are a valuable source for musical meta-data. 1.