This article comprehensively addresses the problem of similarity measurement between music artists via text-based features extracted from Web pages. To this end, we present a thorough evaluation of different term-weighting strategies, normalization methods, aggregation functions, and similarity measurement techniques. In large-scale genre classification experiments carried out on real-world artist collections, we analyze several thousand combinations of settings/parameters that influence the similarity calculation process, and investigate in which way they impact the quality of the similarity estimates. Accurate similarity measures for music are vital for many applications, such as automated playlist generation, music recommender systems, music information systems, or intelligent user interfaces to access music collections by means beyond text-based browsing. Therefore, by exhaustively analyzing the potential of text-based features derived from artist-related Web pages, this article constitutes an important contribution to context-based music information research.

Travelling on a train and looking out of the window at the moving scenery reveals a composition of "visual music" with its own tempo and rhythm, its own colours and harmonies. The project sound/tracks aims at capturing these visual impressions and translates them into a musical composition in real-time - producing an immediate and unique soundtrack to the train journey based on the passing landscape. To this end, the outside impressions are captured with a camera and translated into instantaneously played back piano music. The immediately added sound dimension allows for reflection of the visual impression and deepening of the state of contemplation. For the resulting compositions, the passing scenery can be considered the score. "Re-transcription" of this score to an image gives a panoramic overview over the complete journey and exhibits some interesting effects caused by the movement of the train, such as compression and stretching of passing objects. In addition to intensifying the experience of a train journey, sound/tracks permits to persistently capture and archive the fleeting impressions of journey and composition and allows for re-experiencing the trip both visually and acoustically at a later point.

When travelling on a train, many people enjoy looking out of the window at the landscape passing by. We present sound/tracks, an application that translates the perceived movement of the scenery and other visual impressions, such as passing trains, into music. The continuously changing view outside the window is captured with a camera and translated into MIDI events that are replayed instantaneously. This allows for a reflection of the visual impression, adding a sound dimension to the visual experience and deepening the state of contemplation. The application is intended to be run on both mobile phones (with built-in camera) and on laptops (with a connected Web-cam). We propose and discuss different approaches to translating the video signal into an audio stream, present different application scenarios, and introduce a method to visualise the dynamics of complete train journeys by re-transcribing the captured video frames used to generate the music.

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 collection is arranged along a circular playlist path such that similar sounding pieces are grouped together. As a consequence, regions of musical styles emerge. Furthermore, we propose two approaches to derive informative descriptors that are displayed on the different regions, allowing an overview of the whole collection at a glance. For demonstration, we implemented our prototype interface on an Apple iPod.

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 Information Retrieval. Based on the ID3 tags of a collection of mp3 files, we retrieve relevant Web pages via Google queries and use the contents of these pages to characterize the music pieces and represent them by term vectors. By incorporating complementary information about acous tic similarity we are able to both reduce the dimensionality of the vector space and improve the performance of retrieval, i.e. the quality of the results. Furthermore, the usage of audio similarity allows us to also characterize audio pieces when there is no associated information found on the Web.

We present CoMIRVA, which is an abbreviation for Collection of Music Information Retrieval and Visualization Applications. CoMIRVA is a Java framework and toolkit for information retrieval and visualization. It is licensed under the GNU GPL and can be downloaded from http://www.cp.jku.at/comirva/. At the moment, the main functionalities include music information retrieval, web retrieval, and visualization of the extracted information. In this paper, we focus on the visualization aspects of CoMIRVA. Since many of the information retrieval functions are intended to be applied to problems of the field of music information retrieval (MIR), we demonstrate the functions using data like similarity matrices of music artists gained by analyzing artist-related web pages. CoMIRVA is continuously being extended. Currently, it supports the following visualization techniques: Self-Organizing Map, Smoothed Data Histogram, Circled Bars, Circled Fans, Probabilistic Network, Continuous Similarity Ring, Sunburst, and Music Description Map. Since space is limited, we can only present a selected number of these in this paper. As one key feature of CoMIRVA is its easy extensibility, we further elaborate on how CoMIRVA was used for creating a novel user interface to digital music repositories.

We present a novel interface to (portable) music players that benefit from intelligently structured collections of audio files. For structuring, we calculate similarities between every pair of songs and model a travelling salesman problem (TSP) that is solved to obtain a playlist (i.e., the track ordering during playback) where the average distance between consecutive pieces of music is minimal according to the similarity measure. The similarities are determined using both audio signal analysis of the music tracks and Web-based artist profile comparison. Indeed, we show how to enhance the quality of the well-established methods based on audio signal processing with features derived from Web pages of music artists. Using TSP allows for creating circular playlists that can be easily browsed with a wheel as input device. We investigate the usefulness of four different TSP algorithms for this purpose. For evaluating the quality of the generated playlists, we apply a number of quality measures to two real-world music collections. It turns out that the proposed combination of audio and text-based similarity yields better results than the initial approach based on audio data only. We implemented an audio player as Java applet to demonstrate the benefits of our approach. Furthermore, we present the results of a small user study conducted to evaluate the quality of the generated playlists

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.