  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.
