13,291 research outputs found
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Upbeat and quirky with a bit of a build: Interpretive repertories in creative music search
Pre-existing commercial music is widely used to accom-pany moving images in films, TV commercials and com-puter games. This process is known as music synchronisa-tion. Professionals are employed by rights holders and film makers to perform creative music searches on large catalogues to find appropriate pieces of music for syn-chronisation. This paper discusses a Discourse Analysis of thirty interview texts related to the process. Coded ex-amples are presented and discussed. Four interpretive repertoires are identified: the Musical Repertoire, the Soundtrack Repertoire, the Business Repertoire and the Cultural Repertoire. These ways of talking about music are adopted by all of the community regardless of their interest as Music Owner or Music User.
Music is shown to have multi-variate and sometimes conflicting meanings within this community which are dynamic and negotiated. This is related to a theoretical feedback model of communication and meaning making which proposes that Owners and Users employ their own and shared ways of talking and thinking about music and its context to determine musical meaning. The value to the music information retrieval community is to inform system design from a user information needs perspective
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design
Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh
International Conferences on Pervasive Patterns and Applications (PATTERNS
2015), Mar 2015, Nice, Franc
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Content or context? Searching for musical meaning in task-based interactive information retrieval
Creative professionals search for digital music to accompany moving images using interactive information retrieval systems run by music publishers and record companies. This research investigates the creative professionals and the intermediaries communication processes and information seeking and use behaviour with a view to making recommendations to information retrieval systems builders as to the extent of relative importance of content and contextual factors. A communications model is used to suggest that the meaning of music is determined by its listener and use context, as well as cultural codes and competences. The research is framed by a holistic approach based on Ingwersen and Jarvelin’s Interactive Information Seeking, Retrieval and Behavioral processes model
Analysis and Forecasting of Trending Topics in Online Media Streams
Among the vast information available on the web, social media streams capture
what people currently pay attention to and how they feel about certain topics.
Awareness of such trending topics plays a crucial role in multimedia systems
such as trend aware recommendation and automatic vocabulary selection for video
concept detection systems.
Correctly utilizing trending topics requires a better understanding of their
various characteristics in different social media streams. To this end, we
present the first comprehensive study across three major online and social
media streams, Twitter, Google, and Wikipedia, covering thousands of trending
topics during an observation period of an entire year. Our results indicate
that depending on one's requirements one does not necessarily have to turn to
Twitter for information about current events and that some media streams
strongly emphasize content of specific categories. As our second key
contribution, we further present a novel approach for the challenging task of
forecasting the life cycle of trending topics in the very moment they emerge.
Our fully automated approach is based on a nearest neighbor forecasting
technique exploiting our assumption that semantically similar topics exhibit
similar behavior.
We demonstrate on a large-scale dataset of Wikipedia page view statistics
that forecasts by the proposed approach are about 9-48k views closer to the
actual viewing statistics compared to baseline methods and achieve a mean
average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201
The Music Streaming Sessions Dataset
At the core of many important machine learning problems faced by online
streaming services is a need to model how users interact with the content.
These problems can often be reduced to a combination of 1) sequentially
recommending items to the user, and 2) exploiting the user's interactions with
the items as feedback for the machine learning model. Unfortunately, there are
no public datasets currently available that enable researchers to explore this
topic. In order to spur that research, we release the Music Streaming Sessions
Dataset (MSSD), which consists of approximately 150 million listening sessions
and associated user actions. Furthermore, we provide audio features and
metadata for the approximately 3.7 million unique tracks referred to in the
logs. This is the largest collection of such track metadata currently available
to the public. This dataset enables research on important problems including
how to model user listening and interaction behaviour in streaming, as well as
Music Information Retrieval (MIR), and session-based sequential
recommendations.Comment: 3 pages, introducing a new large scale datase
Bluetooth familiarity: methods of calculation, applications and limitations
We present an approach for utilising a mobile device’s Bluetooth sensor to automatically identify social interactions and relationships between individuals in the real world. We show that a high degree of accuracy is achievable in the automatic identification of mobile devices of familiar individuals. This has implications for mobile device security, social networking and in context aware information access on a mobile device
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