5,777 Hits in 3.0 sec

Combining audio content and social context for semantic music discovery

Douglas R. Turnbull, Luke Barrington, Gert Lanckriet, Mehrdad Yazdani
2009 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09  
When attempting to annotate music, it is important to consider both acoustic content and social context.  ...  This paper explores techniques for collecting and combining multiple sources of such information for the purpose of building a query-by-text music retrieval system.  ...  However, it seems natural that we can improve music IR by combining information related to both the audio content and social context of music.  ... 
doi:10.1145/1571941.1572009 dblp:conf/sigir/TurnbullBLY09 fatcat:4pnr4mgcgfhclfokfgc4sl5vv4

Harvesting and Structuring Social Data in Music Information Retrieval [chapter]

Sergio Oramas
2014 Lecture Notes in Computer Science  
Harvesting and structuring this information semantically would be very useful in context-aware Music Information Retrieval (MIR).  ...  We propose a methodology that combines Social Media Mining, Knowledge Extraction and Natural Language Processing techniques, to extract meaningful context information from social data.  ...  How can we improve retrieval and discovery using the harvested and structured context information?  ... 
doi:10.1007/978-3-319-07443-6_55 fatcat:nbksmdhwj5a2vcyzy76e5tpzie

Knowledge based Semantic Annotation Generation of Music

Sunitha Abburu
2012 International Journal of Computer Applications  
This raises the need for an ontology based annotation generation tool for film songs. The current research designs and implements a tool M-SAGT -Music Semantic Annotation Generation Tool.  ...  This raises the need for semantic based annotation of film songs. Ontology plays a major role in semantic web and information retrieval.  ...  [8] use Ada Boost to automatically generate audio tags for music recommendation. When attempting to annotate music, it is important to consider both acoustic content and social context.  ... 
doi:10.5120/7206-9990 fatcat:l52l55iywrbwtnpvdnaoevcbxu

If You Like Radiohead, You Might Like This Article

Oscar Celma, Paul Lamere
2011 The AI Magazine  
We describe common music recommendation use cases such as finding new artists, finding others with similar listening taste, and generating interesting music playlists.  ...  With so much music readily available, tools that help a user find new, interesting music that matches her taste become increasingly important.  ...  The authors would like to specially thank Keith Emerson and Timothy John Taylor for providing inspiration and encouragement to continue pursuing this subject area. Notes 1.  ... 
doi:10.1609/aimag.v32i3.2363 fatcat:wpnq6gltebch5leyee4rvhqpga

Multimedia information retrieval

Markus Schedl, Emilia Gómez, Masataka Goto
2013 Proceedings of the 21st ACM international conference on Multimedia - MM '13  
Acknowledgments This research is supported by the Austrian Science Fund (FWF): P22856, P25655, and the EU FP7: 601166.  ...  In addition, we provide links to existing software packages and toolboxes for music content-and music context-based feature extraction, similarity computation, knowledge discovery, and music visualization  ...  A key approach in MIR is to describe music via computational features, which can be categorized into: music content, music context, and user context.  ... 
doi:10.1145/2502081.2502237 dblp:conf/mm/SchedlGG13 fatcat:korpleagovgrhjcnjq2cz3q6qu

Culture-Aware Approaches to Modeling and Description of Intonation Using Multimodal Data [chapter]

Gopala Krishna Koduri
2015 Lecture Notes in Computer Science  
As part of this, we propose novel approaches to describe intonation in audio music recordings and to use and adapt the semantic web infrastructure to complement this with the knowledge extracted from text  ...  Over this multimodal knowledge base, we propose similarity measures for the discovery of musical entities, yielding a culturallysound navigation space.  ...  Xavier Serra, and was partly funded by the European Research Council under the European Union's Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583).  ... 
doi:10.1007/978-3-319-17966-7_30 fatcat:jk5w2tfnsbbizmshuyuo5r5yge

Combining Content-Based Auto-Taggers With Decision-Fusion

Emanuele Coviello, Riccardo Miotto, Gert R. G. Lanckriet
2011 Zenodo  
Barrington and T. Bertin-Mahieux for providing the code of [20] and [8] respectively, and acknowledge support from Qualcomm, Inc., Yahoo!  ...  R.M. thanks Nicola Orio for helpful discussion.  ...  and speed up the discovery of desired content.  ... 
doi:10.5281/zenodo.1415665 fatcat:ygviyzc7rbbvbhpwosq3qu6ewi

A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval

Riccardo Miotto, Nicola Orio
2012 ACM Transactions on Information Systems  
Music search and discovery may be carried out using tags, matching user interests and exploiting content-based acoustic similarity.  ...  ACM Reference Format: Miotto R. and Orio N. 2012. A probabilistic model to combine tags and acoustic similarity for music retrieval.  ...  Di Buccio, and Gianmaria Silvello for helpful discussions and support.  ... 
doi:10.1145/2180868.2180870 fatcat:o3lg45gku5eojovrv6ptzbpzs4

Mneclib2 – A Classical Music Digital Library

Zoran Constantinescu, Monica Vlâdoiu
2011 Zenodo  
We present here a DL that we have developed to support users in their quest for classical music pieces within a particular collection of 18,000+ audio recordings.  ...  To cope with the early DL model limitations, we have used a refined socio-semantic and contextual model that allows rich bibliographic content description, along with semantic annotations, reviewing, rating  ...  They also developed a suite of tools for gathering the musical material, for converting between various representations of music, for combined searching based on musical and textual criteria, and for appropriate  ... 
doi:10.5281/zenodo.1070167 fatcat:utfskqsjrvbnpcme2wxi7yqzpa

Music Recommendation: Audio Neighbourhoods to Discover Music in the Long Tail [chapter]

Susan Craw, Ben Horsburgh, Stewart Massie
2015 Lecture Notes in Computer Science  
discovery of unknown and niche music.  ...  A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track's tag-based representation by adding semantic knowledge extracted from the  ...  semantic information such as social tagging from on-line music services.  ... 
doi:10.1007/978-3-319-24586-7_6 fatcat:l35efxq52bceteanuursw6iptm

Comparative Analysis of Content-Based and Context-Based Similarity on Musical Data [chapter]

C. Boletsis, A. Gratsani, D. Chasanidou, I. Karydis, K. Kermanidis
2011 IFIP Advances in Information and Communication Technology  
In this paper, we perform a large scale (20k real music data) similarity measurement using mainstream content and context methodologies.  ...  Commonly used content-based methodologies rely on information that includes little or no semantic information, and thus are reaching a performance "upper bound".  ...  In contrast to content-based attributes of the musical data, context-based information refers to semantic metadata appointed by humans.  ... 
doi:10.1007/978-3-642-23960-1_22 fatcat:ritynweaazffdbl26ulxeytb7q

Good Vibrations: Music Discovery Through Personal Musical Concepts

Vegard Sandvold, Thomas Aussenac, Òscar Celma, Perfecto Herrera
2006 Zenodo  
Acknowledgments The research and development reported here was partially funded by the EU-FP6-IST-507142 SIMAC (Semantic Interaction with Music Audio Content) project.  ...  The contributions made by Bram de Jong, Nicolas Wack, Amaury Hazan, Jose Pedro Garcia and Pedro Cano are highly appreciated.  ...  Foafing the Music [1] is an example of more novel approaches, where user profiles are combined with context-based web information and content-based audio descriptors.  ... 
doi:10.5281/zenodo.1418274 fatcat:nt6mrw6i4vbztmxxqkhhkxx2t4

Content-based Music Recommendation: Evolution, State of the Art, and Challenges [article]

Yashar Deldjoo, Markus Schedl, Peter Knees
2021 arXiv   pre-print
context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start.  ...  The music domain is among the most important ones for adopting recommender systems technology.  ...  for music discovery.  ... 
arXiv:2107.11803v1 fatcat:4hz4hqkkmvcapbdr3wvtp2t4iu

Contextual music information retrieval and recommendation: State of the art and challenges

Marius Kaminskas, Francesco Ricci
2012 Computer Science Review  
Music information retrieval Music recommender systems Context-aware services Affective computing Social computing A B S T R A C T Increasing amount of online music content has opened new opportunities  ...  for implementing new effective information access services -commonly known as music recommender systems -that support music navigation, discovery, sharing, and formation of user communities.  ...  A GMM model was used as a multi-label classifier for both automatic annotation (see Section 4.3.1), and semantic retrieval of audio content.  ... 
doi:10.1016/j.cosrev.2012.04.002 fatcat:eheqwtlpufftni2k4sgolnskli

KISS MIR: Keep It Semantic and Social Music Information Retrieval

Amna Dridi, Mouna Kacimi
2015 Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
We distinguish semantic and social information and use them to build semantic and social profiles for music and users.  ...  More importantly, the combination of semantic and social information is crucial for satisfying user needs.  ...  We propose a personalized ranking model that combine both music-context and user-context which can be semantic, social, or both.  ... 
doi:10.5220/0005616704330439 dblp:conf/ic3k/DridiK15 fatcat:a3l6rfwajvddrpye3xwh6dgoou
« Previous Showing results 1 — 15 out of 5,777 results