The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/220c9304-6a1e-44c6-8c8b-f3a7b635d418

Bloemen, Ties (2024): Code underlying the research project: Extracting absorption coefficients from a room impulse response using a convolutional neural network with domain adaptation. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/220c9304-6a1e-44c6-8c8b-f3a7b635d418.v1

Software

• Artificial Intelligence and Image Processing
• Other Information and Computing Sciences
• Information and Computing Sciences

Absorption Coefficient, Domain adaptation, Machine Learning, Parameter Extraction, Regression, Room Impulse Response

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Ties Bloemen

Extracting absorption coefficients from a room impulse response using a convolutional neural network with domain adaptation

In building design, it is important to consider certain materials for certain acoustical properties. Specifically, the time it takes for an audio signal to decrease in volume by 60 dB is important. This can be estimated with Sabine's and Eyring's formula's, which both make use of the average absorption coefficient of the materials in a room. This absorption coefficient indicates how much of the original audio signal is absorbed into the material. However, measuring these absorption coefficients for a material is difficult and time consuming.

In this study, a machine learning approach is used to estimate the absorption coefficients, by using the room impulse response in combination with the layout of a room. A room impulse response is the characterizing sound of a room. These two pieces of data are processed through a convolutional neural network and a multilayer perceptron, respectively, and combined to make the final prediction of absorption coefficients. A novel approach of simulating the data is used, and a real dataset is used in conjunction with the simulations to use a state-of-the-art regression loss function made for domain adaptation. The results show that the machine learning approach still has a large error compared to using Eyring's formula, and that machine learning is not yet a viable option to use instead of conventional methods. This is the repository for the Research Project where the code to reproduce this subject is housed. Information about installing and using the codebase is available in the README.

• 2024-06-27 first online, published, posted

4TU.ResearchData

The code produces wav files, codebase is written in python, machine learning model is in pth files.

https://github.com/Chutlhu/dEchorate

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science