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A Survey on Probabilistic Models in Human Perception and Machines
Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have developed powerful computational models, including Ba...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805657/ https://www.ncbi.nlm.nih.gov/pubmed/33501252 http://dx.doi.org/10.3389/frobt.2020.00085 |
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author | Li, Lux Rehr, Robert Bruns, Patrick Gerkmann, Timo Röder, Brigitte |
author_facet | Li, Lux Rehr, Robert Bruns, Patrick Gerkmann, Timo Röder, Brigitte |
author_sort | Li, Lux |
collection | PubMed |
description | Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have developed powerful computational models, including Bayesian probabilistic models. However, little true integration between these fields exists in their applications of the probabilistic models for solving analogous problems, such as noise reduction, signal enhancement, and source separation. In this mini review, we briefly introduce and compare selective applications of probabilistic models in machine SP and human psychophysics. We focus on audio and audio-visual processing, using examples of speech enhancement, automatic speech recognition, audio-visual cue integration, source separation, and causal inference to illustrate the basic principles of the probabilistic approach. Our goal is to identify commonalities between probabilistic models addressing brain processes and those aiming at building intelligent machines. These commonalities could constitute the closest points for interdisciplinary convergence. |
format | Online Article Text |
id | pubmed-7805657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78056572021-01-25 A Survey on Probabilistic Models in Human Perception and Machines Li, Lux Rehr, Robert Bruns, Patrick Gerkmann, Timo Röder, Brigitte Front Robot AI Robotics and AI Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have developed powerful computational models, including Bayesian probabilistic models. However, little true integration between these fields exists in their applications of the probabilistic models for solving analogous problems, such as noise reduction, signal enhancement, and source separation. In this mini review, we briefly introduce and compare selective applications of probabilistic models in machine SP and human psychophysics. We focus on audio and audio-visual processing, using examples of speech enhancement, automatic speech recognition, audio-visual cue integration, source separation, and causal inference to illustrate the basic principles of the probabilistic approach. Our goal is to identify commonalities between probabilistic models addressing brain processes and those aiming at building intelligent machines. These commonalities could constitute the closest points for interdisciplinary convergence. Frontiers Media S.A. 2020-07-07 /pmc/articles/PMC7805657/ /pubmed/33501252 http://dx.doi.org/10.3389/frobt.2020.00085 Text en Copyright © 2020 Li, Rehr, Bruns, Gerkmann and Röder. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Li, Lux Rehr, Robert Bruns, Patrick Gerkmann, Timo Röder, Brigitte A Survey on Probabilistic Models in Human Perception and Machines |
title | A Survey on Probabilistic Models in Human Perception and Machines |
title_full | A Survey on Probabilistic Models in Human Perception and Machines |
title_fullStr | A Survey on Probabilistic Models in Human Perception and Machines |
title_full_unstemmed | A Survey on Probabilistic Models in Human Perception and Machines |
title_short | A Survey on Probabilistic Models in Human Perception and Machines |
title_sort | survey on probabilistic models in human perception and machines |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805657/ https://www.ncbi.nlm.nih.gov/pubmed/33501252 http://dx.doi.org/10.3389/frobt.2020.00085 |
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