RT Journal Article
T1 The SONICOM Project: Artificial Intelligence-Driven Immersive Audio, From Personalization to Modeling
A1 Picinali, Lorenzo
A1 Katz, Brian FG
A1 Geronazzo, Michele
A1 Majdak, Piotr
A1 Reyes-Lecuona, Arcadio
A1 Vinciarelli, Alessandro
K1 Neurociencia computacional
K1 Inteligencia artificial
K1 Fisiología
K1 Bioinformática
AB Every individual perceives spatial audio differently, due in large part to the unique and complex shape of ears and head. Therefore, high-quality, headphone-based spatial audio should be uniquely tailored to each listener in an effective and efficient manner. Artificial intelligence (AI) is a powerful tool that can be used to drive forward research in spatial audio personalization. The SONICOM project aims to employ a data-driven approach that links physiological characteristics of the ear to the individual acoustic filters, which allows us to localize sound sources and perceive them as being located around us. A small amount of data acquired from users could allow personalized audio experiences, and AI could facilitate this by offering a new perspective on the matter. A Bayesian approach to computational neuroscience and binaural sound reproduction will be linked to create a metric for AI-based algorithms that will predict realistic spatial audio quality. Being able to consistently and repeatedly evaluate and quantify the improvements brought by technological advancements, as well as the impact these have on complex interactions in virtual environments, will be key for the development of new techniques and for unlocking new approaches to understanding the mechanisms of human spatial hearing and communication.
PB IEEE
SN 1053-5888
YR 2022
FD 2022-10-27
LK https://hdl.handle.net/10630/40615
UL https://hdl.handle.net/10630/40615
LA eng
NO L. Picinali, B. F. Katz, M. Geronazzo, P. Majdak, A. Reyes-Lecuona and A. Vinciarelli, "The SONICOM Project: Artificial Intelligence-Driven Immersive Audio, From Personalization to Modeling [Applications Corner]," in IEEE Signal Processing Magazine, vol. 39, no. 6, pp. 85-88, Nov. 2022, doi: 10.1109/MSP.2022.3182929. keywords: {Spatial audio;Virtual environments;Physiology;Standardization;Predictive models;Data models;Artificial intelligence;Computational neuroscience;Bayes methods},
NO European Union’s Horizon 2020 Research and Innovation Program
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026