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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Published on: July 22, 2025

Indoors audio classification with structure image method for simulating multi-room acoustics.

Erez Shalev¹, Israel Cohen¹, Dmitri Lvov²

¹Andrew and Erna Viterby Faculty of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Technion City, Haifa 3200003, Israel.

The Journal of the Acoustical Society of America

|October 31, 2021

Summary

This summary is machine-generated.

We introduce the structure image method (StIM) for simulating acoustics in multi-room environments. This method efficiently generates impulse responses for training deep learning models in complex spaces.

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Area of Science:

Acoustics
Signal Processing
Machine Learning

Background:

Generating accurate room impulse responses (RIRs) is crucial for acoustic simulations.
Existing methods struggle with complex multi-room structures, limiting deep learning applications.
Deep learning models require large datasets of environmental examples for robust training.

Purpose of the Study:

To introduce an efficient method for generating RIRs in multi-room environments.
To enable large-scale acoustic simulations for training deep learning models.
To demonstrate the effectiveness of the proposed method in audio classification tasks.

Main Methods:

The structure image method (StIM) extends the image method for multi-room acoustics.
StIM efficiently generates numerous environmental examples with low computational complexity.
A framework for integrating StIM-generated environment representations into deep model training is presented.

Main Results:

StIM successfully generated large-scale multi-room acoustic simulations.
The method maintained low computational complexity.
Audio classification models trained with StIM showed promising results for indoor environments with separated sound sources and microphones.

Conclusions:

StIM is an effective and computationally efficient method for simulating multi-room acoustics.
The proposed method facilitates the training of deep learning models for acoustic tasks.
StIM shows particular promise for indoor audio classification scenarios involving cross-room sound propagation.