Environment Sound Classification (ESC) with Choquet Integral Fusion
Document Type
Conference Proceeding
Publication Date
1-24-2022
Department
Department of Computer Science; Department of Mechanical Engineering-Engineering Mechanics
Abstract
The Choquet integral (ChI) plays an important role in the area of aggregating sensors and information. One of the defining advantages of the ChI, compared to other types of aggregations, is that it takes into account how variables interact with one another, which it does by means of what is called a capacity or fuzzy measure. The fuzzy measure captures the relative value, or worth, of different subsets of information sources taken together to make an inference. For data-driven problems, i.e., machine learning, the fuzzy measure comprises the parameters learned for using the ChI. In this work, we apply data-driven ChI decision-level fusion to the problem of classifying sound events from clips of audio. Three benchmark sound classification data sets are utilized: ESC-10, ESC-50, and UrbanSound8K. Six leading classification algorithms-deep networks and transformers-are used as the decision sources. The ChI fusion shows significant gains in accuracy for all three benchmarks.
Publication Title
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
ISBN
9781728190488
Recommended Citation
Wang, Y.,
Havens, T. C.,
&
Barnard, A.
(2022).
Environment Sound Classification (ESC) with Choquet Integral Fusion.
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings.
http://doi.org/10.1109/SSCI50451.2021.9660148
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16683