Literature Survey on Design and Fabrication of Acoustic Sensor with Application of Environment Monitoring

Abstract

This paper presents a review of low-cost acoustic noise monitoring systems based on fundamental electronic and signal conditioning techniques. The reviewed systems utilize microphones to capture environmental sound signals, which are processed through stages such as biasing, amplification, and filtering to obtain stable and usable outputs. The conditioned signals are further analyzed to estimate sound levels, enabling effective observation of acoustic variations. The study focuses on analyzing methods used to improve signal quality and measurement reliability through better control of analog parameters such as gain and filtering. Compared to conventional low-cost systems, the reviewed approaches emphasize enhanced signal conditioning to reduce unwanted disturbances and improve consistency in measurements. The study highlights simple, cost-effective, and practical acoustic sensing techniques using core electronic components. This review provides an overview of efficient and adaptable acoustic sensing systems suitable for applications such as environmental monitoring, laboratory analysis, and small-scale industrial observation, with scope for future enhancement through advanced processing and extended functionalities.

Introduction

The text reviews the problem of noise pollution caused by urbanization and industrial growth, highlighting its harmful health effects and the need for continuous, large-scale monitoring systems. Traditional sound level meters are accurate but expensive and unsuitable for widespread deployment, motivating the development of low-cost acoustic sensing systems using microphones, microcontrollers, and signal processing techniques.

Most low-cost systems rely on signal conditioning (amplification, filtering, biasing) and RMS-based SPL estimation to measure noise levels. Recent research has focused on improving accuracy, scalability, and real-time monitoring using IoT, wireless sensor networks (WSN/WASN), MEMS microphones, and embedded DSP systems.

The literature survey shows a wide range of approaches:

• IoT and wireless networks enable real-time distributed monitoring and noise mapping across cities and industrial sites.
• MEMS-based sensors and microcontrollers provide low-cost, scalable solutions with acceptable accuracy (often within ±1–2 dB).
• Advanced signal processing and DSP systems improve precision and allow computation of multiple acoustic parameters.
• Machine learning and classification models enhance noise event detection and analysis.
• High-end research systems achieve excellent accuracy but are often complex, power-intensive, or costly.

Across studies, key challenges remain, including environmental noise interference, calibration stability, limited sensitivity at low sound levels, data transmission load, power constraints, and scalability issues. Many systems also focus mainly on measuring overall sound levels rather than detailed noise classification.

Conclusion

This paper presented a review of low-cost acoustic sensing systems based on fundamental electronic signal conditioning techniques for environmental noise monitoring applications. The reviewed studies demonstrate that microphones combined with biasing, amplification, filtering, and basic signal processing techniques can effectively capture and analyze environmental sound signals. Various approaches discussed in the literature highlight the importance of proper signal conditioning, calibration, and filtering in improving signal quality, measurement stability, and overall system performance. The reviewed systems also show that low-cost acoustic sensing techniques provide simple, practical, and cost-effective solutions suitable for applications such as environmental monitoring, laboratory analysis, smart city systems, and small-scale industrial observation. Future improvements can focus on enhancing sensor sensitivity, optimizing dynamic range, improving filtering techniques, and increasing measurement accuracy under different environmental conditions. Additional advancements in signal processing, wireless communication, and scalable monitoring architectures can further improve the performance and adaptability of low-cost acoustic sensing systems for real-world applications.

References

[1] P. Kumar, S. K. N, M. H. N, R. K. S, and C. B. J, “Noise Level Monitor and Controlling System,” International Research Journal of Engineering and Technology (IRJET), vol. 12, no. 6, 2025. [2] A. Author(s), “Real Time Industrial Noise Mapping with IoT Systems,” International Journal of Computer Applications, vol. 186, no. 43, 2024. [3] Y. Yoo and B.-D. Choi, “Readout Circuits for Capacitive Sensors,” Micromachines, vol. 12, no. 8, p. 960, 2021. [4] B. Peng, K. I.-K. Wang and W. H. Abdulla, “Environmental Noise Monitoring Using Distributed Hierarchical Wireless Acoustic Sensor Network,” Internet of Things, vol. 28, 2024. [5] Y. Shang et al., “Research Progress in Distributed Acoustic Sensing Techniques,” Sensors, vol. 22, no. 16, p. 6060, 2022. [6] A. Scandurra et al., “A Review of Design Approaches for Low-Frequency Noise Measurement Systems,” Review of Scientific Instruments, vol. 93, 2022. [7] J. Picaut et al., “Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review,” Sensors, vol. 20, no. 8, p. 2256, 2020. [8] R. M. Alsina-Pagès et al., “Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities,” Sensors, 2020. [9] J. M. López et al., “A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities,” Sensors, vol. 20, 2020. [10] R. M. Alsina-Pagès et al., “Design of a Low-Cost Configurable Acoustic Sensor for the Rapid Development of Sound Recognition Applications,” Electronics, vol. 9, no. 7, p. 1155, 2020. [11] L. Luo et al., “Wireless Sensor Networks for Noise Measurement and Acoustic Event Recognitions in Urban Environments,” Sensors, vol. 20, p. 2093, 2020. [12] F. Alías and R. M. Alsina-Pagès, “Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities,” Journal of Sensors, 2019. [13] P. Prince et al., “Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring,” Sensors, vol. 19, p. 553, 2019. [14] L. Hallett, M. Tatum, G. Thomas, S. Sousan, K. Koehler, and T. Peters, “An Inexpensive Sensor for Noise,” Journal of Occupational and Environmental Hygiene, vol. 15, no. 5, pp. 448–454, 2019. [15] J. Ardouin, L. Charpentier, M. Lagrange, F. Gontier, N. Fortin, J. Picaut, D. Ecotière, G. Guillaume, and C. Mietlicki, “An Innovative Low Cost Sensor for Urban Sound Monitoring,” in Proc. Euronoise, 2018. [16] C. Lang, J. Fang, H. Shao, X. Ding, and T. Lin, “High-Sensitivity Acoustic Sensors from Nanofibre Webs,” Nature Communications, vol. 7, p. 11108, 2016. [17] F. Domínguez et al., “Active Self-Testing Noise Measurement Sensors for Large-Scale Environmental Sensor Networks,” Sensors, vol. 13, pp. 17241–17264, 2013. [18] A. Bertrand, “Applications and Trends in Wireless Acoustic Sensor Networks: A Signal Processing Perspective,” 2011. [19] I. Kivela et al., “Design of Noise Measurement Sensor Network: Networking and Communication,” IEEE, 2011. [20] I. Kivela et al., “Design of Networked Low-Cost Wireless Noise Measurement Sensors,” Sensors & Transducers Journal, 2010.

Copyright

Copyright © 2026 Prarthana P Bhat, Sharanya S M, Shraddha E N, Tejaswini L Yadav, Dr. Electa Alice Jayarani A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.