MusicoNet: A Social Network for Musicians Based on the Internet of Musical Things and People Paradigm

To date, there are a number of online social networks dedicated to musicians. However, these do not truly leverage and capitalize on the musical diversity, i.e., the variability that exists across musicians, their instruments, musical activities and social relations. For instance, existing social networks are not designed to search and find for musicians with specific characteristics related to their profile and, above all, the actual particuliarities of their playing style. More importantly, their integration with the Internet of Musical Things (IoMusT) has been largely overlooked thus far. To bridge these gaps, in this paper we propose MusicoNet, a social network for musicians based on IoMusT technologies. The network leverages Semantic Web methods and is made accessible through an app for smartphones and tablet devices, which can wirelessly interact with smart musical instruments (or, through a laptop, with conventional instruments). MusicoNet was not conceived to exchange topic-oriented communication through textual and photographic posts as it occurs in popular social networks, but to support the search for and connectivity among musicians having given diversity factors. Such a search is not simply based on sole textual queries as it occurs in conventional social networks, but also on content-based queries which can be performed via musical instruments. We describe the technical implementation of MusicoNet, the IoMusT ecosystem it enables, and a preliminary technical validation. We then discuss the lessons learned and future avenues for the proposed technology, which represents the first instance of the recently proposed Internet of Musical Things and People paradigm.

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Publication Details

Published
2024-09-30
DOI
https://doi.org/10.1109/is262782.2024.10704117
Primary Topic
Music and Audio Processing
Type
article
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article

MusicoNet: A Social Network for Musicians Based on the Internet of Musical Things and People Paradigm

Luca Turchet, Jacopo Tomelleri, Alessandro Ruffo
Music and Audio Processing
article

MusicoNet: A Social Network for Musicians Based on the Internet of Musical Things and People Paradigm

Luca Turchet, Jacopo Tomelleri, Alessandro Ruffo
article en

Abstract

To date, there are a number of online social networks dedicated to musicians. However, these do not truly leverage and capitalize on the musical diversity, i.e., the variability that exists across musicians, their instruments, musical activities and social relations. For instance, existing social networks are not designed to search and find for musicians with specific characteristics related to their profile and, above all, the actual particuliarities of their playing style. More importantly, their integration with the Internet of Musical Things (IoMusT) has been largely overlooked thus far. To bridge these gaps, in this paper we propose MusicoNet, a social network for musicians based on IoMusT technologies. The network leverages Semantic Web methods and is made accessible through an app for smartphones and tablet devices, which can wirelessly interact with smart musical instruments (or, through a laptop, with conventional instruments). MusicoNet was not conceived to exchange topic-oriented communication through textual and photographic posts as it occurs in popular social networks, but to support the search for and connectivity among musicians having given diversity factors. Such a search is not simply based on sole textual queries as it occurs in conventional social networks, but also on content-based queries which can be performed via musical instruments. We describe the technical implementation of MusicoNet, the IoMusT ecosystem it enables, and a preliminary technical validation. We then discuss the lessons learned and future avenues for the proposed technology, which represents the first instance of the recently proposed Internet of Musical Things and People paradigm.

University of Trento (IT)
Reduced inequalities
Openalex Percentile: Top 81%
Music and Audio Processing
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