An innovative three-dimensional user interface for exploring music collections enriched

Knees, Peter; Schedl, Markus; Pohle, Tim; Widmer, Gerhard

doi:10.1145/1180639.1180652

Cited by 58 publications

(34 citation statements)

References 15 publications

(14 reference statements)

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“…These similarity measures enable applications such as music recommender systems [4,8], automated playlist generators [20,25], or intelligent user interfaces to music collections [23,19]. Computational features for music similarity calculation can be broadly categorized into music content-based, music context-based, and user context-based [34].…”

Section: Motivationmentioning

confidence: 99%

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

2013

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Microtexts are a valuable, albeit noisy, source to infer collaborative information. As music plays an important role in many human lives, microblogs on musicrelated activities are available in abundance. This paper investigates different strategies to estimate music similarity from these data sources. In particular, we first present a framework to extract co-occurrence scores between music artists from microblogs and then investigate 12 similarity estimation functions to subsequently derive resemblance scores. We evaluate the approaches on a collection of microblogs crawled from Twitter over a period of 10 months and compare them to standard tf-idf approaches. As evaluation criteria we use precision and recall in an artist retrieval task as well as rank proximity. We show that collaborative chatter on music can be effectively used to develop music artist similarity measures, which are a core part of every music retrieval and recommendation system. Furthermore, we analyze the effects of the ''long tail'' on retrieval results and investigate whether results are consistent over time, using a second dataset.

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Section: Motivationmentioning

confidence: 99%

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

2013

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“…This version also features a simple playlist generation method. In [25,24] a three-dimensional extension is proposed to explore music collections in a fun way by further incorporating additional material mined from the Web. In addition, this three-dimensional version features intuitive playlist generation.…”

Section: Categorizing Music Retrieval Systemsmentioning

confidence: 99%

Personalization in Multimodal Music Retrieval

Schedl

Knees

2013

Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation

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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.

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“…Researchers have also developed techniques for organizing music by extracting audio signal features according their similarity. Kness et al [10] introduced a method to simulate a given collection of music as a virtual landscape. Music items are clustered based on their similarity.…”

Section: Existing Approachesmentioning

confidence: 99%

A Mobile Interface for Hierarchical Information Visualization and Navigation

Hao

Zhang

2007

2007 IEEE International Symposium on Consumer Electronics

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There is a dramatic increase in the population who use mobile computing devices. Though the hardware becomes more powerful, effective support for information rendering on small screens very much lags behind. To display hierarchical information, researchers have proposed many algorithms for desktop screen visualization. Such algorithms are generally divided into connection and enclosure. Connection approach displays hierarchy with a clear structure but consume display area. Enclosure can maximally utilize the screen space but the layout is essentially implicit. This paper describes a new Radial Edgeless Tree (RELT) for visualizing hierarchical data on palmsized devices. The aim of RELT is to combine the advantages of connection and enclosure approaches. By recursively partitioning the display area, this technique maximizes the space usage. The structural clarity of layout can be reached by arranging location of non-overlapping regions. RELT can be adapted for visualizing structural information for various applications.

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An innovative three-dimensional user interface for exploring music collections enriched

Cited by 58 publications

References 15 publications

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

Personalization in Multimodal Music Retrieval

A Mobile Interface for Hierarchical Information Visualization and Navigation

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