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Spatial impulse response analysis and ensemble learning for efficient precision level sensing.
- Source :
- Discover Artificial Intelligence; 9/26/2024, Vol. 4 Issue 1, p1-17, 17p
- Publication Year :
- 2024
-
Abstract
- In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis with machine learning (ML) techniques, offering a unique and effective approach for sound-based classification that can be extended to various domains beyond waste management. By employing a buzzer-generated sine sweep signal, we create a distinctive signature specific to the fill level of the waste container. This signature, once accurately decoded, is then interpreted by a specially developed ensemble learning algorithm. Our approach achieves a classification accuracy of over 90% when implemented locally on a development board, optimizing operational efficiencies and eliminating the need to delegate complex classification tasks to external entities. Using low-cost and energy-efficient hardware components, our method offers a cost-effective approach that contributes to sustainable and efficient waste management practices, providing a reliable and locally deployable solution. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
REFUSE containers
WASTE management
IMPULSE response
CLASSIFICATION
Subjects
Details
- Language :
- English
- ISSN :
- 27310809
- Volume :
- 4
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Discover Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 179948170
- Full Text :
- https://doi.org/10.1007/s44163-024-00165-w