Semantic Crowdsourcing of Soundscapes Heritage: A Mojo Model for Data-Driven Storytelling

Abstract

The current paper focuses on the development of an enhanced Mobile Journalism (MoJo) model for soundscape heritage crowdsourcing, data-driven storytelling, and management in the era of big data and the semantic web. Soundscapes and environmental sound semantics have a great impact on cultural heritage, also affecting the quality of human life, from multiple perspectives. In this view, context- and location-aware mobile services can be combined with state-of-the-art machine and deep learning approaches to offer multilevel semantic analysis monitoring of sound-related heritage. The targeted utilities can offer new insights toward sustainable growth of both urban and rural areas. Much emphasis is also put on the multimodal preservation and auralization of special soundscape areas and open ancient theaters with remarkable acoustic behavior, representing important cultural artifacts. For this purpose, a pervasive computing architecture is deployed and investigated, utilizing both client- and cloud-wise semantic analysis services, to implement and evaluate the envisioned MoJo methodology. Elaborating on previous/baseline MoJo tools, research hypotheses and questions are stated and put to test as part of the human-centered application design and development process. In this setting, primary algorithmic backend services on sound semantics are implemented and thoroughly validated, providing a convincing proof of concept of the proposed model.