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 Daniel Wolff

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Average citations per article1.17
Citation Count7
Publication count6
Publication years2011-2017
Available for download4
Average downloads per article127.25
Downloads (cumulative)509
Downloads (12 Months)343
Downloads (6 Weeks)22
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1 published by ACM
January 2017 Journal on Computing and Cultural Heritage (JOCCH) - Special Issue on Digital Infrastructure for Cultural Heritage, Part 1: Volume 10 Issue 1, April 2017
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 18,   Downloads (12 Months): 307,   Downloads (Overall): 307

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In musicology and music research generally, the increasing availability of digital music, storage capacities, and computing power enable and require new and intelligent systems. In the transition from traditional to digital musicology, many techniques and tools have been developed for the analysis of individual pieces of music, but large-scale music ...
Keywords: Digital musicology, music information retrieval, big data, semantic web

2 published by ACM
September 2014 DLfM '14: Proceedings of the 1st International Workshop on Digital Libraries for Musicology
Publisher: ACM
Citation Count: 0
Downloads (6 Weeks): 1,   Downloads (12 Months): 7,   Downloads (Overall): 26

Full text available: PDFPDF
Conducting experiments on large scale musical datasets often requires the definition of a dataset as a first step in the analysis process. This is a classification task, but metadata providing the relevant information is not always available or reliable and manual annotation can be prohibitively expensive. In this study we ...

3 published by ACM
September 2014 DLfM '14: Proceedings of the 1st International Workshop on Digital Libraries for Musicology
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 2,   Downloads (12 Months): 23,   Downloads (Overall): 84

Full text available: PDFPDF
Digital music libraries and collections are growing quickly and are increasingly made available for research. We argue that the use of large data collections will enable a better understanding of music performance and music in general, which will benefit areas such as music search and recommendation, music archiving and indexing, ...

April 2014 Information Retrieval: Volume 17 Issue 2, April 2014
Publisher: Kluwer Academic Publishers
Citation Count: 0

Computational modelling of music similarity is an increasingly important part of personalisation and optimisation in music information retrieval and research in music perception and cognition. The use of relative similarity ratings is a new and promising approach to modelling similarity that avoids well known problems with absolute ratings. In this ...
Keywords: Metric learning to rank, Neural networks, Support vector machines, Relative similarity ratings, Music similarity, Metric learning

5 published by ACM
April 2012 WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 6,   Downloads (Overall): 92

Full text available: PDFPDF
Predicting user's tastes on music has become crucial for a competitive music recommendation systems, and perceived similarity plays an influential role in this. MIR currently turns towards making recommendation systems adaptive to user preferences and context. Here, we consider the particular task of adapting music similarity measures to user voting ...
Keywords: similarity adaptation, human factors, music information retrieval

July 2011 AMR'11: Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
Publisher: Springer-Verlag
Citation Count: 4

In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature ...
Keywords: music information retrieval, music recommendation, computational modelling, music perception, music similarity

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