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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65396

AI Audio Classify Recycle Bin (Smart Dustbin)

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65396

Volume:

10 |

6 |

Jun 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Govindraj Ambatwar

Govindraj Ambatwar

Department of electronics and telecommunication

Sinhgad College of engineering

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Priti Shelar

Priti Shelar

Department of electronics and telecommunication

Sinhgad College of engineering

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Omkar Dawkhar

Omkar Dawkhar

Department of electronics and telecommunication

Sinhgad College of engineering

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Mentors

PMA

Prof Ms A.B.Kale

Department of electronics and telecommunication

Sinhgad College of engineering

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Full ArticlesAcceptance Certificate

Abstract

The increasing global concern for environmental sustainability has prompted the development of innovative tech-nologies aimed at optimizing waste management processes. In this context, the AI Audio Classify Recycle Bin emerges as a groundbreaking solution, leveraging artificial intelligence to enhance recycling practices. The system captures and analyzes audio signals produced by disposed items through integrated mi-crophones and classifies materials such as glass, plastic, metal, and paper based on their distinct sound signatures. This real-time, decentralized approach ensures efficient sorting directly within the bin, minimizing contamination and promoting proper recycling practices. With benefits including increased accuracy, environmental sustainability, reduced operational costs, and user-friendly interfaces, this technology represents a significant advancement toward a more environmentally conscious and effective waste management system.

KEYWORDS

Artificial Intelligence Audio Classification Waste Management Edge AI Semantic Audio Analysis Signal Pro-cessing Algorithms Audio Pattern Recognition IoT (Internet of Things) Sound Recognition Edge Computing Sensor Technol-ogy Recycling Bin Material Identification.

Cite This Article

Govindraj Ambatwar, Priti Shelar, Omkar Dawkhar, Prof Ms A.B.Kale, "AI Audio Classify Recycle Bin (Smart Dustbin)," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-4, Jun 2026.

Full Article/Conclusion

The AI Audio Classify Recycle Bin project is an innovative and sustainable solution for modern waste management. By combining artificial intelligence with audio-based waste classifi-cation, it automates the process of waste segregation, improves recycling efficiency, and reduces environmental pollution. The system’s ability to operate entirely at the edge without relying on cloud connectivity makes it robust, fast, and suitable for deployment in diverse environments.

References

[1] Md. Wahidur Rahman, Rahabul Islam, Arafat Hasan et al., “Intelligent waste management system using deep learning with IoT,” 2020. [Hardware: Camera Module, Servo Motor, Raspberry Pi.] [2] N. Abou Baker, P. Szabo-Müller, and U. Handmann, “Transfer learning-based method for automated e-waste recycling in smart cities,” 2021. [Applies fine-tuned AlexNet on 12-class e-waste dataset.] [3] X. Li and R. Grammenos, “A smart recycling bin using waste image classification at the edge,” 2022. [Image classification ML on embedded systems.] [4] B. Fang, J. Yu, Z. Chen, A. I. Osman et al., “Artificial intelligence for waste management in smart cities,” 2023. [Sensor-based monitoring, prediction models, robotic arm.] [5] H. I. Abdel-Shafy and M. S. M. Mansour, “Solid waste issue: sources, composition, disposal, recycling, and valorization,” Egyptian Journal of Petroleum, vol. 27, no. 4, pp. 1275–1290, 2018. [6] M. Abbasi and A. El Hanandeh, “Forecasting municipal solid waste generation using artificial intelligence modelling approaches,” Waste Manag., vol. 56, pp. 13–22, 2016. [7] T. Abunama, F. Othman, M. Ansari, and A. El-Shafie, “Leachate generation rate modeling using artificial intelligence algorithms,” Environ. Sci. Pollut. Res., vol. 26, pp. 3368–3381, 2019. [8] V. E. Akpan and D. O. Olukanni, “Hazardous waste management: an African overview,” Recycling, vol. 5, p. 15, 2020. [9] L. Andeobu, S. Wibowo, and S. Grandhi, “Artificial intelligence applications for sustainable solid waste management practices in Australia: a systematic review,” Sci. Total Environ., vol. 834, p. 155389, 2022. [10] S. Ascher, I. Watson, and S. You, “Machine learning methods for modelling the gasification and pyrolysis of biomass and waste,” Renew. Sustain. Energy Rev., vol. 155, p. 111902, 2022. [11] J. Bobulski and M. Kubanek, “Vehicle for plastic garbage gathering,” in Proc. ICECCME 2021, pp. 1–5, 2021. [12] L. Amal, L. H. Son, and H. Chabchoub, “SGA: spatial GIS-based genetic algorithm for route optimization of municipal solid waste collection,” Environ. Sci. Pollut. Res., vol. 25, pp. 27569–27582, 2018.

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Publication Date

6/24/2026

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Review Rounds: 2

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