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FrAMBI: A Software Framework for Auditory Modeling Based on Bayesian Inference.

Roberto Barumerli1,2, Piotr Majdak3

  • 1Acoustics Research Institute, Austrian Academy of Sciences, Dominikanerbastei 15, Vienna, 1010, Austria. roberto.barumerli@oeaw.ac.at.

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Summary
This summary is machine-generated.

This study introduces FrAMBI, a new auditory modeling framework using Bayesian inference to standardize sound perception research. FrAMBI enhances reproducibility and facilitates comparisons across studies in hearing science.

Keywords:
Auditory modelingBayesian statisticsBehavioral simulationComputational neuroscienceModel-based analysisSound localization

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Area of Science:

  • Neuroscience
  • Hearing Science
  • Computational Auditory Neuroscience

Background:

  • Auditory models are crucial for understanding listener behavior and neural mechanisms in acoustics.
  • Current auditory modeling practices lack standardization, hindering reproducibility and cross-study comparisons.
  • This limits the advancement of research into the neural mechanisms of sound perception.

Purpose of the Study:

  • To introduce FrAMBI (Framework for Auditory Modeling based on Bayesian Inference), a novel MATLAB/Octave toolbox.
  • To provide a standardized framework for implementing auditory models based on the perception-action cycle.
  • To enable automatic statistical analysis of behavioral data for studying neural mechanisms of sound perception.

Main Methods:

  • Development of FrAMBI, a toolbox integrated with the Auditory Modeling Toolbox (AMT).
  • Implementation of auditory models using Bayesian inference within a standardized perception-action cycle structure.
  • Demonstration of FrAMBI's capabilities through sound source localization tasks with varying complexity.

Main Results:

  • FrAMBI successfully facilitates the implementation of auditory models for static and dynamic acoustic scenarios.
  • The framework supports the definition and comparison of multiple model variants to test different neural mechanisms.
  • Parameter estimation and model comparison procedures are integrated within the FrAMBI toolbox.

Conclusions:

  • FrAMBI offers a standardized approach to auditory modeling, promoting reproducible research in neuroscience.
  • The toolbox enhances the ability to study neural mechanisms underlying sound perception and listener behavior.
  • Long-term maintenance and expansion within AMT will foster continued advancements in the field.