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Title:Towards incorporating the notion of feature shape in music and text retrieval
Author(s):Cheng, Yi-Yun; Weigl, David M.; Downie, J. S.; Page, Kevin R.
Subject(s):Feature data
feature shape
Music Information Retrieval
HathiTrust
Abstract:Extracted feature data augment information resources with concrete characterizations of their content, but only approximate to the meaningful high-level descriptions typically expected by digital musicology scholars (domain experts with some technological affinity, but with no expertise in signal processing or feature data). Feature shapes provide abstract aggregations of feature types which share common characteristics when applied in extraction workflows. We explore the feasibility of feature shape-based filter- ing and querying within a large audio dataset of live music performances, employing operation sequences as specified by the Audio Feature Ontology and Vocabulary. We further implement analogous semantic structures for the HathiTrust Extracted Feature Dataset to demonstrate the general applicability of feature shapes in music and text retrieval.
Issue Date:2018-03
Publisher:iConference
Citation Info:Cheng, Y.-Y., Weigl, D. M., Downie, J. S., Page, K. R. (2018). Towards incorporating the notion of feature shape in music and text retrieval. In Proceedings of the iConference 2018 Annual Meeting, Sheffield, U.K., March 25-28.
Genre:Conference Poster
Type:Text
Image
Language:English
URI:http://hdl.handle.net/2142/99611
Date Available in IDEALS:2018-04-05


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