ReM-AM: Reflective middleware for acoustic management in intelligent environments

Santiago, Gabriela; Aguilar, José; Chávez, Danilo

doi:10.1109/clei.2017.8226380

Cited by 1 publication

(3 citation statements)

References 14 publications

(14 reference statements)

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“…[2,3], and it was used as a base for the development of the ReM-AM architecture described in Ref. [4], where the layers are the same, except for the Audio Management Layer (AML) that was added for ReM-AM.…”

Section: Amicl and Rem-ammentioning

confidence: 99%

“…[1], the Reflective Middleware for Acoustic Management (ReM-AM) extends AmICL [2,3] to change the behavior of any SE from the acoustic perspective, implementing different components for sound management. In ReM-AM [4], there are three components: collecting audio Data (CAD) to create metadata and categorize sound information, interaction of system-user-agent (ISUA) to identify and analyze the information and determine the source and interactions and the decision-making (DM) to adapt the SE in terms of acoustic requirements. These components help the system to deploy a set of autonomic cycles of data analysis tasks [5,6], which work together to exploit the acoustic in the self-organization of the SE.…”

Section: Introductionmentioning

confidence: 99%

“…These components help the system to deploy a set of autonomic cycles of data analysis tasks [5,6], which work together to exploit the acoustic in the self-organization of the SE. The previous work [7] introduced a state of the art of acoustics and its relationship with SEs, while [4] described the ReM-AM middleware. The autonomic cycles supported by ReM-AM are explained in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Ontological model for the acoustic management in a smart environment

Santiago

Aguilar

2022

ACI

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Purpose The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.Design/methodology/approach This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.Findings This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).Research limitations/implications Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.Practical implications In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.Originality/value The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

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Section: Amicl and Rem-ammentioning

confidence: 99%