Bayesian Methods in Acoustical Science and Engineering
The Journal of the Acoustical Society of America (JASA) and JASA Express Letters are calling for papers for a joint Special Issue on “Bayesian Methods in Acoustical Science and Engineering.”
Bayesian analysis has been increasingly applied to a wide variety of acoustical research and engineering tasks. Bayesian probability theory provides acousticians with an elegant framework for inferential data analysis which facilitates learning from acoustic experimental investigations that provide a deeper understanding of the underlying theory. In these analysis tasks, certain prior knowledge is often available about the acoustical phenomena under investigation, based either on the underlying physical theory or on certain phenomenological relationships. Bayesian probability theory allows this available information to be incorporated in the processing and analysis and exploited in the Bayesian framework as physical or phenomenological models. Many analysis tasks in acoustics often include two levels of inference, model selection and parameter estimation. Bayesian inferential methods provide solutions to these two levels of inference by extensively using Bayes’ theorem within a unified framework.
This Special Issue encourages submissions of original works on various model-based approaches recently applied to signal processing and analysis in acoustics using either one or both levels of inference.
Please note: Accepted papers will be published in the next available regular issue of the selected journal and identified as belonging to this Special Issue. After all papers from both journals have been published for the Special Issue, they will all be listed in a joint online collection on the JASA and JASA Express Letters websites.
Topics covered include, but are not limited to:
- Solutions/investigations of inverse problems using Bayesian approaches
- Optimization and Parameter estimations
- Uncertainty Quantification using Bayesian approaches
- Bayesian approaches in Machine Learning and Artificial Intelligence
- Physics-Informed or Physics-Based Deep Neural Networks (PINN, PBDNN) in Acoustic applications
- Advanced sampling methods used in multidimensional parameter spaces within Bayesian approaches
- Maximum entropy applied in Bayesian framework
- Bayesian causal analysis
Guest Editors
Ning Xiang*
Rensselaer Polytechnic Institute, USA
Email: xiangn@rpi.edu
Ali Mohammad-Djafari
CNRS, Centrale Supelec, Gif-sur-Yvette, France
Email: djafari@ieee.org
Eliza (Z-H.) Michalopoulou
New Jersey Institute of Technology, USA
Email: michalop@njit.edu
*Liaison Guest Editor
Manuscript Details & Submission
Please note: although there will be a joint online collection, your paper, if accepted, will publish in the journal you chose to submit to.
- Please select the journal you will be submitting to and visit the following, where you will find the Information for Contributors for the selected journal and link to this journal’s online submission system:
- For JASA: JASA Preparing Your Manuscript page
- For JASA Express Letters: JASA Express Letters Preparing Your Manuscript page
- To ensure your submission is considered for the Special Issue, please do the following:
- In your manuscript file, before the abstract, please include the following: “This paper is part of a special issue on Bayesian Methods in Acoustical Science and Engineering.”
- When submitting your paper in the selected journal’s online submission system:
- Select the appropriate article type for your manuscript (see the selected journal’s Information for Contributors for details on article types).
- Select the name of this special issue (SPECIAL ISSUE ON BAYESIAN METHODS IN ACOUSTICAL SCIENCE AND ENGINEERING ) in the dropdown menu for the section/category