Abstract. We present a user interface (UI) for browsing collections of web pages about music artists. Given such a collection, we use a term list to index its contents and to derive term co-occurrences. Based on these co-occurrences, we create a UI that employs a variant of the Sunburst visualization technique. The UI is embedded in CoMIRVA, our framework for music information retrieval and visualization. We use two dictionaries of musically relevant terms and derive information about which terms occur on which web pages. Based on this information, subsets of the web page collection are created according to the terms that occur most frequently in the collection. The generated UI, which we call the Co-Occurrence Browser (COB), thus allows for associating each artist with its most important (descriptive) terms and for browsing the respective web pages. To assess the usability of the COB, we carried out a small qualitative user study. Furthermore, different term weighting functions used to create the UI were tested and evaluated in a quantitative user study. 1

This paper presents an implementation of a simple playlist generator. An audio-based music similarity measure and simple heuristics are used to create playlists given minimum user input. The ultimate goal of this work is to conduct a field study, i.e., to run the system on the users ’ personal collection and study the usage behavior over a longer period of time. The functions include, for example, allowing the user to control the variance of the playlists in terms of how often the same song or songs from the same artists are repeated. 1.

In music information retrieval (MIR), there are two main research directions, which are based either on a folder hierarchy and metadata, or on the actual acoustic content. We believe that both content-based and hierarchy-based retrieval have their respective strengths for browsing and organizing music collections, and that the integration of content analysis techniques in metadata-based media UIs can lead to more powerful UIs. In this paper we present a prototype, in which audio analysis techniques and user feedback are integrated into an interactive UI for browsing and organizing large music collections. We also provide visual assistance to support non-visual perception of music. We discussed our system with test users and received encouragement as well as valuable suggestions for future research. ACM Classification: H5.2 [Information interfaces and presentation]:

We present a novel user interface that offers a fun way to explore music collections in virtual landscapes in a gamelike manner. Extending previous work, special attention is paid to scalability and user interaction. In this vein, the ever growing size of today’s music collections is addressed in two ways that allow for visualizing and browsing nearly arbitrarily sized music repositories. First, the proposed user interface deepTune employs a hierarchical version of the Self-Organizing Map (SOM) to cluster similar pieces of music using multiple, hierarchically aligned layers. Second, to facilitate orientation in the landscape by presenting well-known anchor points to the user, a combination of Web-based and audio signal-based information extraction techniques to determine cluster prototypes (songs) is proposed. Selecting representative and well-known prototypes – the former is ensured by using signal-based features, the latter by using Web-based data – is crucial for browsing large music collections. We further report on results of an evaluation carried out to assess the quality of the proposed cluster prototype ranking.

Many interesting pieces of music violate established structures or rules of their genre on purpose. These songs can be very atypical in their interior structure and their different parts might actually allude to entirely different other songs or genres. We present a query-by-example-based user interface that shows songs related to the one currently playing. This relation is not based on overall similarity, but on the similarity between the part currently playing and parts of other songs in the collection along different dimensions (pitch, timbre, bars, beats, loudness). The similarity is initially computed automatically, but can be corrected by the user. Once a sufficient number of corrections has been made, we expect the similarity measure to reach an even higher precision. Our system thereby allows users to discover hidden similarities on the level of song sections instead of whole songs. 1.

Abstract. This paper presents an approach to browse collections of web pages about music artists by means of descriptive terms and multimedia content. To this end, a user interface called Three-Dimensional Co-Occurrence Browser (3D-COB) is introduced. 3D-COB automatically extracts and weights terms from artist-related web pages. This textual information is complemented with information on the multimedia content found on the web pages. For the user interface of 3D-COB, we elaborated a three-dimensional extension of the Sunburst visualization technique. The hierarchical data to be visualized is obtained by analyzing the web pages for combinations of co-occurring terms that are highly ranked by a term weighting function. As for evaluation, we investigated different term weighting strategies in a first user study. A second user study was carried out to assess ergonomic aspects of 3D-COB, especially its usefulness for gaining a quick overview of a set of web pages and for efficiently browsing within this set. 1

Abstract This article presents an approach to browse collections of web pages about music artists by means of descriptive terms and multimedia content. To this end, a user interface called Three-Dimensional Co-Occurrence Browser (3D-COB) is introduced. 3D-COB automatically extracts and weights terms from artist-related web pages. This textual information is complemented with information on the multimedia content found on the web pages. For the user interface of 3D-COB, we elaborated a three-dimensional extension of the Sunburst visualization technique. The hierarchical data to be visualized is obtained by analyzing the web pages for combinations of co-occurring terms that are highly ranked by a term weighting function. We further investigated, in a first user study, different term weighting strategies to generate the visualization. A second user study was carried out to assess ergonomic aspects of 3D-COB, especially its usefulness for gaining a quick overview of a set of web pages and for efficiently browsing within this set.

Abstract. This position paper provides an overview of current research endeavors and existing solutions in multimodal music retrieval, where the term “multimodal ” relates to two aspects. The first one is taking into account the music context of a piece of music or an artist, while the second aspect tackled is that of the user context. The music context is introduced as all information important to the music, albeit not directly extractable from the audio signal (such as editorial or collaboratively assembled meta-data, lyrics in textual form, cultural background of an artists, or images of album covers). The user context, in contrast, is defined by various external factors that influence how a listener perceives music. It is therefore strongly related to user modeling and personalization, both facets of music information research that have not gained large attention by the MIR community so far. However, we are confident that adding personalization aspects to existing music retrieval systems (such as playlist generators, recommender systems, or visual browsers) is key to the future of MIR. In this vein, this contribution aims at defining the foundation for future research directions and applications related to multimodal music information systems. 1

There is a growing interest in touch-based and gestural interfaces as alternatives to the dominant mouse, keyboard and monitor interaction. Content and context-aware visualizations of audio collections have been proposed as a more effective way to interact with the increasing amounts of audio data available digitally. Audioscapes is a framework for prototyping and exploring how touch-based and gestural controllers can be used with state-of-the-art content and context-aware visualizations. By providing well-defined interfaces and conventions a variety of different audio collections, controllers and visualization methods can be combined to create innovative ways of interacting with large audio collections. We describe the overall system architecture, the currently available components and specific case studies. 1.

In the past few years the computational capabilities of mobile phones have been constantly increasing. Frequently these smartphones are also used as portable music players. In this paper we describe GeoShuffle – a prototype system for content-based music browsing and exploration that targets such devices. One of the most interesting aspects of these portable devices is the inclusion of positioning capabilities based on GPS. GeoShuffle adds location-based and time-based context to a user’s listening preferences. Playlists are dynamically generated based on the location of the user, path and historical preferences. Browsing large music collections having thousands of tracks is challenging. The most common method of interaction is using long lists of textual metadata such as artist name or genre. Current smartphones are characterized by small screen real-estate which limits the amount of textual information that can be displayed. We propose selforganizing tag clouds, a 2D tag cloud representation that is based on an underlying self-organizing map calculated using automatically extracted audio features. To evalute the system the Magnatagatune database is utilized. The evaluation indicates that location and time context can improve the quality of music recommendation and that selforganizing tag clouds provide faster browsing and are more engaging than text-based tag clouds. 1.