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  1. Proceedings of the 3rd International workshop on Digital Libraries for Musicology (DLfM 2016)
  2. Mining metadata from the web for AcousticBrainz
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Representing and Linking Music Performance Data with Score Information
Approaches to handwritten conductor annotation extraction in musical scores
MORTY: A Toolbox for Mode Recognition and Tonic Identification
Document Analysis for Music Scores via Machine Learning
In Collaboration with In Concert: Reflecting a Digital Library as Linked Data for Performance Ephemera
Exploring J-DISC: Some Preliminary Analyses
A standard format proposal for hierarchical analyses and representations
Digitizing musical scores: Challenges and opportunities for libraries
Data Generation and Multi-Modal Analysis for Recorded Operatic Performance
Mining metadata from the web for AcousticBrainz
The Music Addressability API: A draft specification for addressing portions of music notation on the web

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Mining metadata from the web for AcousticBrainz

Content Provider ACM Digital Library
Author Bogdanov, Dmitry Porter, Alastair Serra, Xavier
Abstract Semantic annotations of music collections in digital libraries are important for organization and navigation of the collection. These annotations and their associated metadata are useful in many Music Information Retrieval tasks, and related fields in musicology. Music collections used in research are growing in size, and therefore it is useful to use semi-automatic means to obtain such annotations. We present software tools for mining metadata from the web for the purpose of annotating music collections. These tools expand on data present in the AcousticBrainz database, which contains software-generated analysis of music audio files. Using this tool we gather metadata and semantic information from a variety of sources including both community-based services such as MusicBrainz, Last.fm, and Discogs, and commercial databases including Itunes and AllMusic. The tool can be easily expanded to collect data from a new source, and is automatically updated when new items are added to AcousticBrainz. We extract genre annotations for recordings in AcousticBrainz using our tool and study the agreement between folksonomies and expert sources. We discuss the results and explore possibilities for future work.
Starting Page 53
Ending Page 56
Page Count 4
File Format PDF
ISBN 9781450347518
DOI 10.1145/2970044.2970048
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-08-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Databases Genre Music information retrieval
Content Type Text
Resource Type Article
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