ABSTRACT. This paper provides a systematic literature review of studies investigating deep learning-based ambient sound processing and real-time data tracking tools, metaverse and immersive technologies, and image detection and computer vision algorithms. The analysis highlights that virtual navigation and automated speech recognition tools, machine learning and computer vision algorithms, and digital twinning and multisensor fusion technologies shape extended reality environments and interactive digital worlds. Throughout December 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “the decentralized and interconnected metaverse” + “predictive modeling and visual perception algorithms,” “ambient sound recognition and processing tools,” and “deep neural network and vision sensing technologies.” As I inspected research published between 2022 and 2023, only 168 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 36, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

Keywords: predictive modeling and visual perception algorithms; ambient sound recognition and processing tools; deep neural network and vision sensing technologies; metaverse

How to cite: Clayton, E. (2023). “Predictive Modeling and Visual Perception Algorithms, Ambient Sound Recognition and Processing Tools, and Deep Neural Network and Vision Sensing Technologies in the Decentralized and Interconnected Metaverse,” Linguistic and Philosophical Investigations 22: 145–161. doi: 10.22381/lpi2220239.

Received 26 January 2023 • Received in revised form 25 May 2023
Accepted 27 May 2023 • Available online 30 May 2023