TY - JOUR
T1 - Knowledge discovery and visualisation framework using machine learning for music information retrieval from broadcast radio data
AU - Furner, Michael
AU - Islam, Md Zahidul
AU - Li, Chang Tsun
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/11/15
Y1 - 2021/11/15
N2 - Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.
AB - Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.
KW - Data and knowledge visualization
KW - Data mining
KW - Machine learning
KW - Record classification
KW - Signal processing systems
KW - Software Architectures
KW - Sound and music computing
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U2 - 10.1016/j.eswa.2021.115236
DO - 10.1016/j.eswa.2021.115236
M3 - Article
AN - SCOPUS:85108077200
SN - 0957-4174
VL - 182
SP - 1
EP - 11
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 115236
ER -