Examinando por Autor "Lenzi, Sara"

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    The design of Datascapes: toward a design framework for sonification for anomaly detection in AI-supported networked environments
    (Frontiers Media SA, 2024-01-11) Lenzi, Sara; Terenghi, Ginevra; Meacci, Damiano; Moreno Fernández de Leceta, Aitor; Ciuccarelli, Paolo
    There is a growing need for solutions that can improve the communication between anomaly detection algorithms and human operators. In the context of real-time monitoring of networked systems, it is crucial that new solutions do not increase the burden on an already overloaded visual channel. Sonification can be leveraged as a peripheral monitoring tool that complements current visualization systems. We conceptualized, designed, and prototyped Datascapes, a framework project that explores the potential of sound-based applications for the monitoring of cyber-attacks on AI-supported networked environments. Within Datascapes, two Design Actions were realized that applied sonification on the monitoring and detection of anomalies in (1) water distribution networks and (2) Internet networks. Two series of prototypes were implemented and evaluated in a real-world environment with eight experts in network management and cybersecurity. This paper presents experimental results on the use of sonification to disclose anomalous behavior and assess both its gravity and the location within the network. Furthermore, we define and present a design methodology and evaluation protocol that, albeit grounded in sonification for anomaly detection, can support designers in the definition, development, and validation of real-world sonification applications.
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    Ítem
    Perceived quality of a nighttime hospital soundscape
    (Walter de Gruyter GmbH, 2024-08-01) Lenzi, Sara; Lindborg, PerMagnus; Spagnol, Simone; Kamphuis, Daan; Özcan, Elif
    The hospital soundscape is known for high noise levels and a perception of chaos, leading to concerns about its impact on patients, families, professionals, and other hospital staff. This study investigates the relationship between sound, Annoyance, and sleep quality in a multi-patient neurology ward. A mixed-methods approach was employed. Interviews were conducted with medical staff (n = 7) to understand their experiences with sound. Questionnaires and sleep tracking devices (n = 20) assessed patient sleep quality and Annoyance caused by sound events. In addition, listeners (n = 28) annotated 429 nighttime audio recordings to identify sound sources and rate Annoyance level, which we considered the key emotional descriptor for patients. Over 9,200 sound events were analysed. While snoring, a patient-generated sound dominated the nighttime soundscape and was highly rated for Annoyance, and staff-generated sounds such as speech and footsteps were found to contribute more to accumulated Annoyance due to their extended duration. This study suggests that patient sleep quality can be improved by focusing on design interventions that reduce the impact of specific sounds. These might include raising awareness among staff about activities that might produce annoying sounds and implementing strategies to mitigate their disruptive effects.
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    Ítem
    Re(de)fining sonification: project classification strategies in the Data Sonification Archive
    (Audio Engineering Society, 2024-09) Lindborg, PerMagnus; Caiola, Valentina; Ciuccarelli, Paolo; Chen, Manni; Lenzi, Sara
    This study focuses on a corpus of 445 sonification projects currently available in the Data Sonification Archive (DSA). The DSA develops in a collaborative process that involves researchers and creative communities and has been online since early 2021. Projects are heuristically classified according to several aspects, in particular their intended purpose, targeted users, subject matter, sonification method, and combination of media. In the present study, the authors analyze six curatorial classification strategies, labelled Goal, Method, User, Macro Topic, Micro Topic, and MediaMix, and discuss their definitions and usefulness for the archive. They then introduce two computational classification strategies, respectively based on clustering of music information retrieval of sonification audio and topic modeling of the descriptive texts that accompany DSA projects. Correlation analysis between curatorial and computational classifications, correspondingly sized, showed that the text-based method was more powerful than the audio-based methods. The authors then explored predictive modeling, tentatively achieving results for Goal, Method, and Macro Topic. This points toward the potential for automatic classification to assist in the curatorial management of the archive, as well as for similar repositories. The discussion focuses on how analysis of classification strategies supports a broadening of the definition of sonification, both as theoretical construct and as practice, where the communicative intention of the author, the aesthetic quality of the listening experience, a more explicit focus on narrative patterns, and other emerging aspects within sonification design, are all contributing factors to transitioning the field toward a mass medium for data representation, communication, and meaning-making.