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Data-driven and Machine Learning-based Room Acoustic Estimation and Modeling

Guest Editors:
Paul Calamia: Reality Labs Research (Meta), USA 
Enzo De Sena: University of Surrey, UK
Wenyu Jin: Cruise LLC, USA
Toon van Waterschoot: KU Leuven, Belgium

Submission Status: Closed   |   Submission Deadline: Closed

EURASIP Journal on Audio, Speech, and Music Processing is calling for submissions to our Collection on Data-driven and Machine Learning-based Room Acoustic Estimation and Modeling. 

Reverberation is a near-constant presence in our daily lives, and being able to estimate, synthesize, control or suppress reverberation is therefore important. The field of room-acoustic modeling is rapidly adopting data-driven methods, including machine learning (ML), due to technical advancements in related fields and the availability of increasingly larger amounts of computing power and data. Given enough data, mathematical models can be trained to discover underlying acoustic representations useful to perform the task at hand. However, compared to more traditional signal-processing methods, ML and data-driven methods also bring a multitude of unique challenges to consider to increase their utility, generalisability, reliability, and interpretability.

  1. Dynamic parameterization of acoustic environments has drawn widespread attention in the field of audio processing. Precise representation of local room acoustic characteristics is crucial when designing audio ...

    Authors: Chunxi Wang, Maoshen Jia, Meiran Li, Changchun Bao and Wenyu Jin
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2024 2024:23
  2. Audio augmented reality (AAR), a prominent topic in the field of audio, requires understanding the listening environment of the user for rendering an authentic virtual auditory object. Reverberation time (

    Authors: Shivam Saini, Isaac Engel and Jürgen Peissig
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2024 2024:16

About the collection

This special issue seeks contributions that address some of these challenges in the following areas:

● Room acoustic parameter estimation 
● Room geometry estimation
● Room acoustic synthesis and data augmentation
● Room impulse response interpolation and extrapolation
● Room impulse response identification
● Dereverberation and room acoustic inverse problems
● Physics-informed data-driven modeling of room acoustics
● Multi-modal methods, including e.g. modeling approaches using audio-visual observations

Image credit: © Alexandr Mitiuc /

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Data-driven and Machine Learning-based Room Acoustic Estimation and Modeling" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.