Adaptive Multimedia Retrieval: Semantics, Context, and by Andreas Nürnberger, Sebastian Stober, Birger Larsen, Marcin

By Andreas Nürnberger, Sebastian Stober, Birger Larsen, Marcin Detyniecki (eds.)

This e-book constitutes the completely refereed post-conference court cases of the tenth overseas convention on Adaptive Multimedia Retrieval, AMR 2012, held in Copenhagen, Denmark, in October 2012.

The 17 revised complete papers awarded have been conscientiously reviewed and chosen from various submissions. The papers disguise themes of state-of-the-art contributions, good points and category, place context, language and semantics, tune retrieval, and adaption and HCI.

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Additional info for Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation: 10th International Workshop, AMR 2012, Copenhagen, Denmark, October 24-25, 2012, Revised Selected Papers

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Russell-Rose et al. Fig. 22. com user experience architecture but the activities are clearly recognizable. The ‘Show conversation’ function discussed above also reflects direct support to Locate, Explore and Comprehend a conversation object as a single interaction. com experience is so strongly centred on sense-making, search and discovery modes often directly constitute the activity paths connecting one object to another within the user experience architecture. com, and are directly visible to an unprecedented degree in the interaction design built on that skeleton.

MIDI and humdrum, appears in over 18 % (81) of these references [1–5,11,13–15,26,34,43, 52,53,65–67,69,70,72,80–83,86,87,94,95,120,128,156–160,166,180,184,185,192, 196, 197, 199, 202, 203, 218–221, 229, 240, 243, 245, 251, 253, 257, 259, 261, 279–281, 283–286,293,335–341,346,363,385,399,413,428,434,435]. , lyrics, co-occurrences on the WWW, album covers, and so on [25, 40, 62, 98, 125, 142, 272–277, 283–286, 308, 309, 354, 374, 381, 415, 438, 448, 458, 464,465]. Figure 3 shows the number of experiments in the evaluative work we survey using datasets with specific numbers of labels.

657–662 (2011) 13. : Genre classification using harmony rules induced from automatic chord transcriptions. In: Proceedings of the ISMIR (2009) 14. : First-order logic classification models of musical genres based on harmony. In: Proceedings of the SMC (2009) 15. : Improving music genre classification using automatically induced harmony rules. J. New Music Res. 39(4), 349–361 (2010) 16. : Audio feature engineering for automatic music genre classification. In: Proceedings of the Recherche d’Information Assist´ee par Ordinateur, Pittsburgh, Pennsylvania, pp.

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