Spatial Impulse Response Analysis and Ensemble Learning for Efficient Precision Level Sensing

TL;DR

This paper introduces a novel sound-based method combining spatial impulse response analysis and ensemble learning to accurately determine waste container fill levels, achieving over 90% accuracy with low-cost hardware.

Contribution

It presents a new integrated approach for fill level sensing using sound signatures and machine learning, optimized for local deployment and efficiency.

Findings

Achieves over 90% classification accuracy.

Uses low-cost, energy-efficient hardware.

Provides a scalable solution for waste management.

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.