A Sound Source Location Method Based on Time Difference of Arrival with Improved Dung Beetle Optimizer

Song Chunning, Zhang Jindong
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:165-176, 2024.

Abstract

In microphone array sound source localization based on Time Difference of Arrival (TDOA), traditional methods for solving the nonlinear equations of TDOA lead to significant deviations and lower accuracy. To address this issue, this paper proposes a TDOA-based sound source localization method using an Improved Dung Beetle Optimizer (IDBO) algorithm. This method enhances the performance of the Dung Beetle Optimizer (DBO) by employing strategies such as chaotic mapping, golden sine, and adaptive tdistribution, and applies it to sound source localization. To evaluate the performance of the IDBO, it is compared with DBO, Harris Hawk Optimizer (HHO), Gray Wolf Optimizer (GWO), Bald Eagle Search (BES) algorithm, and Whale Optimization Algorithm (WOA). The results showed that in solving benchmark functions and localization models, it demonstrates faster convergence speed, higher localization accuracy, and better stability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-chunning24a, title = {A Sound Source Location Method Based on Time Difference of Arrival with Improved Dung Beetle Optimizer}, author = {Chunning, Song and Jindong, Zhang}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {165--176}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/chunning24a/chunning24a.pdf}, url = {https://proceedings.mlr.press/v245/chunning24a.html}, abstract = {In microphone array sound source localization based on Time Difference of Arrival (TDOA), traditional methods for solving the nonlinear equations of TDOA lead to significant deviations and lower accuracy. To address this issue, this paper proposes a TDOA-based sound source localization method using an Improved Dung Beetle Optimizer (IDBO) algorithm. This method enhances the performance of the Dung Beetle Optimizer (DBO) by employing strategies such as chaotic mapping, golden sine, and adaptive tdistribution, and applies it to sound source localization. To evaluate the performance of the IDBO, it is compared with DBO, Harris Hawk Optimizer (HHO), Gray Wolf Optimizer (GWO), Bald Eagle Search (BES) algorithm, and Whale Optimization Algorithm (WOA). The results showed that in solving benchmark functions and localization models, it demonstrates faster convergence speed, higher localization accuracy, and better stability.} }
Endnote
%0 Conference Paper %T A Sound Source Location Method Based on Time Difference of Arrival with Improved Dung Beetle Optimizer %A Song Chunning %A Zhang Jindong %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-chunning24a %I PMLR %P 165--176 %U https://proceedings.mlr.press/v245/chunning24a.html %V 245 %X In microphone array sound source localization based on Time Difference of Arrival (TDOA), traditional methods for solving the nonlinear equations of TDOA lead to significant deviations and lower accuracy. To address this issue, this paper proposes a TDOA-based sound source localization method using an Improved Dung Beetle Optimizer (IDBO) algorithm. This method enhances the performance of the Dung Beetle Optimizer (DBO) by employing strategies such as chaotic mapping, golden sine, and adaptive tdistribution, and applies it to sound source localization. To evaluate the performance of the IDBO, it is compared with DBO, Harris Hawk Optimizer (HHO), Gray Wolf Optimizer (GWO), Bald Eagle Search (BES) algorithm, and Whale Optimization Algorithm (WOA). The results showed that in solving benchmark functions and localization models, it demonstrates faster convergence speed, higher localization accuracy, and better stability.
APA
Chunning, S. & Jindong, Z.. (2024). A Sound Source Location Method Based on Time Difference of Arrival with Improved Dung Beetle Optimizer. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:165-176 Available from https://proceedings.mlr.press/v245/chunning24a.html.

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