2013
DOI: 10.1007/978-3-642-37425-8_5
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Personalization in Multimodal Music Retrieval

Abstract: 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 m… Show more

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Cited by 8 publications
(9 citation statements)
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“…The authors give some examples of such context categories and particular aspects: social and cultural context (political and national context), everyday situations (work, leisure, consumer, entertainment), presence/absence of others (live, audience, recorder). It is exactly this multifaceted and individual way of music perception that has largely been neglected so far when elaborating and evaluating music retrieval approaches, but should be given more attention, in particular considering the trend towards personalized and context-aware systems (Liem et al 2011;Schedl and Knees 2011).…”
Section: Fig 1 Factors That Influence Human Music Perceptionmentioning
confidence: 99%
See 1 more Smart Citation
“…The authors give some examples of such context categories and particular aspects: social and cultural context (political and national context), everyday situations (work, leisure, consumer, entertainment), presence/absence of others (live, audience, recorder). It is exactly this multifaceted and individual way of music perception that has largely been neglected so far when elaborating and evaluating music retrieval approaches, but should be given more attention, in particular considering the trend towards personalized and context-aware systems (Liem et al 2011;Schedl and Knees 2011).…”
Section: Fig 1 Factors That Influence Human Music Perceptionmentioning
confidence: 99%
“…According to Schedl and Knees (2011), these aspects can be grouped into three different categories: music content, music context, and user context. Here we extend our previous categorization (Schedl and Knees 2011) by a fourth set of aspects, the user properties. Examples for each category are given in Fig.…”
mentioning
confidence: 99%
“…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]. While contentbased feature extraction techniques derive the representation of a music item from the audio signal itself [7], music context-based approaches make use of data that are not encoded in the audio signal [30], for instance, the performer's political background, the meaning of a song's lyrics, images of album covers, or co-occurrence information derived from playlists.…”
Section: Motivationmentioning
confidence: 99%
“…We will further investigate how similarity information derived from microblog data compares to similarity estimation techniques that exploit other data sources, such as the audio signal, music-related web pages, or song lyrics. Moreover,we plan to account for temporal dynamics in the microblog data, which matches nicely our ultimate research aim to elaborate personalized and user-aware music retrieval and discovery systems that take into account different levels of personalization (individual, peer group, city, country) [45]. The research at hand is also closely related to trend de-5 http://www.cp.jku.at/people/schedl/datasets.html tection from social media sources.…”
Section: Discussionmentioning
confidence: 97%
“…In contrast, feature extraction and similarity measurement approaches that take into account the user's context are relatively sparse throughout the scientific literature. An overview of user context features likely to be useful for MIR tasks is presented in [45].…”
Section: Introductionmentioning
confidence: 99%