TY - GEN
T1 - Environment Sound Classification (ESC) with Choquet Integral Fusion
AU - Wang, Yilin
AU - Havens, Timothy C.
AU - Barnard, Andrew
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1109/SSCI50451.2021.9660148
DO - 10.1109/SSCI50451.2021.9660148
M3 - Conference contribution
AN - SCOPUS:85125777718
T3 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
BT - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Y2 - 5 December 2021 through 7 December 2021
ER -