TELKOMNIKA Telecommunication Computing Electronics and Control Vol.
No.
April 2026, pp.
ISSN: 1693-6930.
DOI: 10.
12928/TELKOMNIKA.
Low-cost ESP32-based sound data acquisition system with MATLAB integration for real-time noise monitoring Reymark-John A.
Macapanas1.
Adrian P.
Galido2.
Apple Rose B.
Alce3 Department of Information Technology.
College of Information Technology and Computing.
University of Science and Technology of Southern Philippines-Villanueva Campus.
Villanueva Misamis Oriental.
Philippines Department of Information Systems.
College of Computer Studies (CCS).
Mindanao State University Iligan Institute of Technology (MSU-IIT).
Iligan City.
Philippines Department of Computer Applications.
College of Computer Studies (CCS).
Mindanao State University Iligan Institute of Technology (MSU-IIT).
Iligan City.
Philippines Article Info
ABSTRACT
Article history:
This study presents the design and implementation of a low-cost ESP32-based sound data acquisition system (SDAS) for real-time noise monitoring.
The system integrates a micro-electro-mechanical systems (MEMS) microphone for accurate acoustic data capture, an ESP-WROOM-32 microcontroller for signal processing and wireless data transmission, and MATLAB for real-time visualization and analysis.
Designed and simulated in KiCAD 8.
0, the SDAS includes a microSD module for local data backup and offline analysis.
The system was tested in four indoor locations within Mindanao State University Ae Iligan Institute of Technology, recording mean noise levels ranging from 2 dB in laboratory environments to 32.
1 dB in classrooms, with corresponding standard deviations of 1.
2Ae7.
0 dB.
Expert evaluation from eight assessors confirmed the systemAos usability, data reliability, and The system demonstrates effective monitoring for both quiet and dynamic settings.
Limitations include single-node configuration, indoor-only testing, and MATLAB-based USB data transfer.
Despite these, the proposed SDAS provides a scalable and reproducible model for smart campus and urban environmental monitoring, supporting sustainable development goals (SDG) 3, 9, and 11.
Received Sep 17, 2025 Revised Oct 21, 2025 Accepted Dec 8, 2025 Keywords:
ESP32
MATLAB
Micro-electro-mechanical systems microphone Noise pollution Real-time monitoring Sound data acquisition system This is an open access article under the CC BY-SA license.
Corresponding Author:
Reymark-John A.
Macapanas Department of Information Technology.
College of Information Technology and Computing University of Science and Technology of Southern Philippines-Villanueva Campus 9002 Villanueva.
Misamis Oriental.
Philippines Email: reymarkjohn.
macapanas@ustp.
INTRODUCTION
Noise pollution is a growing global concern, especially in urban environments, where industrial activities and transportation systems significantly contribute to acoustic disturbances.
Prolonged exposure to high noise levels has been linked to adverse health effects, such as cardiovascular diseases, stress, and impaired cognitive performance, as noted by the World Health Organization (WHO).
In the context of smart cities and sustainable development, real-time environmental monitoring systems are crucial in supporting evidence-based policies that address these challenges.
Numerous research initiatives have explored the use of internet of thing (IoT)-based noise monitoring Picaut et al.
reviewed low-cost urban noise monitoring networks, while Mydlarz et al.
proposed smart wireless acoustic sensor network architectures.
Vidaya-Vila et al.
demonstrated low-cost urban acoustic monitoring devices, and Fatema et al.
presented a real-time IoT-based noise monitoring system.
Journal homepage: http://journal.
id/index.
php/TELKOMNIKA A ISSN: 1693-6930 However, these studies often rely on high-cost devices or lack localized, modular implementation strategies that are easy to replicate in developing regions.
Previous studies have achieved significant progress in IoT-based acoustic monitoring.
however, many solutions remain costly, non-modular, or complex to replicate, especially in developing regions.
Systems that rely on proprietary platforms or high-end sensors restrict scalability and accessibility.
To address these challenges, the proposed sound data acquisition system (SDAS) introduces a low-cost, modular, and reproducible architecture that integrates affordable hardware components, such as the ESP32 microcontroller and micro-electro-mechanical systems (MEMS) microphone, with MATLAB-based visualization for efficient data analysis.
This approach aims to bridge the gap between commercial noise meters and research-level monitoring systems, enabling universities and local institutions to adopt scalable acoustic sensing solutions for smart environments.
This research addresses those limitations by designing a SDAS that is both low-cost and modular, integrating an ESP32 microcontroller.
MEMS microphone, and MATLAB-based visualization to form an affordable yet robust monitoring solution.
Unlike previous works, this study emphasizes replicability, offline data backup, and hardware modularity using KiCAD 8.
The main contributions are: .
the development of a scalable SDAS for real-time acoustic monitoring, .
integration of ESP32.
MEMS microphone, and MATLAB, and .
validation of the system through expert evaluation and quantitative analysis.
METHOD
The methodology (Figure .
for the SDAS follows a six-phase structured design process to ensure reproducibility, functionality, and reliability.
Phase 0 involves identifying the noise pollution problem.
Phase 1 defines system objectives.
Phase 2 involves requirement analysis and component selection.
Phase 3 addresses schematic design using KiCAD 8.
Phase 4 integrates data acquisition with MATLAB for real-time Phases 5 and 6 focus on system testing and expert evaluation under campus environments.
System topology The SDAS operates in four stages: .
data acquisition: a MEMS microphone captures environmental noise in real time and forwards the signal for processing .
data processing: the ESP-WROOM-32 converts the analog signal to digital data for analysis or storage .
local data storage: processed data is written to a microSD card to ensure availability during network interruptions and enable offline retrieval .
MATLAB integration: via serial connection, data is streamed to MATLAB for real-time plotting and trend analysis .
This topology allows independent operation with offline storage and MATLAB-based analysis for comprehensive evaluation .
Figure 2 illustrates the end-to-end data flow from acquisition to Figure 1.
SDAS methodology flowchart Figure 2.
System topology TELKOMNIKA Telecommun Comput El Control.
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April 2026: 599-607 TELKOMNIKA Telecommun Comput El Control Hardware design This section outlines the methods employed in the design and development of the SDAS, from conceptualization to system integration.
Figure 3.
illustrates the power and programming interface of the SDAS, while Figure 3.
presents the sound module schematic.
The microcontroller configuration is shown in Figure 3.
, the microSD card interface is detailed in Figure 3.
, and the relay control circuit is depicted in Figure 3.
Together, these sub-figures present the complete schematic design of the proposed SDAS.
Each labeled section is explained as:
Hardware component justification The system design incorporates carefully selected hardware components:
Oe MEMS microphone: high sensitivity and low power, captures environmental noise in decibels .
Oe ESP-WROOM-32: core microcontroller for acquisition, analog-to-digital conversion, and wireless/serial data transfer to MATLAB .
Oe MicroSD module: provides non-volatile local storage for backup and offline analysis .
Oe Schrack RT1 relay: manages power distribution, extending battery life and preventing surges .
Printed circuit board (PCB) and schematic design The complete hardware design was implemented in KiCAD 8.
The schematic consists of:
Oe Power and programming interface: handles voltage regulation and firmware upload .
Oe Sensor interface: connects the MEMS microphone for analog signal input .
Oe Microcontroller interface: central processing unit responsible for all logic operations.
Oe MicroSD storage interface: manages data writing and retrieval.
Oe Relay control circuit: automates power switching.
The PCB layout prioritizes compactness and signal integrity, with careful routing to minimize interference.
The 3D model shows component placement, supporting easy manufacturing and assembly .
The PCB design Figure 4 integrates the ESP-WROOM-32 microcontroller.
MEMS microphone, power relay, and microSD slot in a compact layout.
It includes connectors for external devices, light-emitting diode (LED) indicators for system status, and regulated power management to ensure stable operation.
Careful trace routing minimizes interference and signal loss, allowing the SDAS to function efficiently and reliably for urban noise monitoring applications.
The 3D model Figure 5 shows the placement of the ESP-WROOM-32.
MEMS microphone.
Schrack RT1 relay, and microSD slot on the PCB.
It also includes input/output (I/O) connectors.
LED indicators, and a reset button for system feedback and control.
The design ensures compact assembly, durability, and reliable operation for noise data acquisition in urban environments.
MATLAB data flow MATLAB was used for serial communication, visualization, and real-time analysis (Figure .
The SDAS transmits data via universal serial bus (USB) serial, which MATLAB interprets through a defined communication (COM) port .
Once a stable connection is established, incoming values are plotted using MATLABAos built-in tools .
The process runs continuously until the user terminates the session, ensuring real-time monitoring and visualization.
Testing and evaluation The SDAS was deployed in four locations within the Mindanao State University Iligan Institute of Technology (MSU-IIT) college of computer studies (IoT lab, graduate lab, embedded systems lab, and a classroo.
to assess performance under varying conditions .
Noise data was collected over one-hour intervals, consistent with recommended monitoring practices to capture temporal variability .
MATLAB
plots recorded mean, peak, and standard deviation values.
Usability and accuracy were validated by eight subject matter experts using a standard rubric.
Key criteria included: .
ease of system deployment, .
accuracy of noise data captured, .
clarity of MATLAB visualizations, and .
power efficiency and robustness of the hardware.
Feedback confirmed the SDAS to be reliable for monitoring both low-activity and dynamic environments .
Calibration and validation To ensure the reliability of recorded noise data, the SDAS output was calibrated against a reference sound level meter (SLM) following ISO 1996-2:2017 acoustic standards.
Calibration was conducted by generating controlled sound levels between 20 dB and 80 dB using a standard audio source.
The SDAS readings were compared to the SLM measurements at 5 dB intervals.
A linear regression model was applied, yielding Low-cost ESP32-based sound data acquisition system with MATLAB A (Reymark-John A.
Macapana.
A ISSN: 1693-6930 an RA value of 0.
984, confirming strong correlation and minimal deviation (<2.
3 dB mean erro.
This calibration ensured accurate noise measurement prior to deployment in campus environments.
Figure 3.
Schematic design of the SDAS: .
power and programming interface.
sound module.
microcontroller configuration.
microSD card interface.
relay control circuit Figure 4.
PCB design of SDAS Figure 5.
3D model of SDAS TELKOMNIKA Telecommun Comput El Control.
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April 2026: 599-607 TELKOMNIKA Telecommun Comput El Control Figure 6.
MATLAB data flow
RESULTS AND DISCUSSION
The SDAS was tested across four indoor locations: the IoT lab, the graduate laboratory, the embedded laboratory, and a classroom within the college of computer studies.
MSU-IIT.
The testing involved a consistent one-hour sampling period at each location.
Results were graphically plotted using MATLAB, with key metrics such as average noise level, peak noise, and standard deviation calculated.
IoT laboratory Figure 7.
Noise levels in the IoT lab were consistently low .
Ae15 dB) with occasional peaks up to 18.
5 dB, indicating a quiet, controlled environment typical of research spaces.
Minor fluctuations were caused by short activities such as equipment adjustments or foot traffic.
The small standard deviation confirms suitability for high-precision work, though occasional noise management may be needed during active hours.
Graduate laboratory Figure 8 noise levels in the graduate lab fluctuated between 20Ae45 dB, with frequent peaks reflecting group discussions, equipment use, and movement.
The higher variability compared to the IoT lab indicates a more dynamic setting.
Peaks aligned with interaction periods, while quieter intervals reflected downtime.
Overall, the labAos active use highlights the need to balance collaboration with measures that reduce disruptive noise during focused tasks.
Embedded laboratory Figure 9, noise levels in the embedded lab ranged from 20Ae34 dB with moderate, consistent fluctuations caused by equipment use, tool adjustments, and technical discussions.
Although variability was less than in the graduate lab, activity was higher than in the IoT lab.
The space reflects a balance between hands-on experimentation and acoustic stability.
Classroom Figure 10, the classroom showed the most dynamic noise profile, fluctuating between 22Ae 50 dB with frequent peaks caused by continuous lectures, discussions, and movement.
High variability reflects an active academic setting.
These findings emphasize the need for acoustic treatment or scheduling strategies to maintain clarity and concentration during instruction.
Figure 7.
Noise data 2nd floor building IoT lab Figure 9.
Noise data 3rd floor building embedded Figure 8.
Noise data 3rd floor building graduate lab Figure 10.
Noise Data 3rd floor building classrooms Low-cost ESP32-based sound data acquisition system with MATLAB A (Reymark-John A.
Macapana.
A ISSN: 1693-6930 Indoor testing Testing was conducted across four indoor environments-IoT lab, graduate lab, embedded lab, and classroom-each for one hour: .
IoT lab: mean 14.
2 dB, standard deviation (SD) 1.
2, .
graduate lab: mean 5 dB.
SD 6.
8, .
embedded lab: mean 25.
3 dB.
SD 4.
5, and .
classroom: mean 32.
1 dB.
SD 7.
The MATLAB-generated plots demonstrated accurate and real-time visualization of environmental noise, confirming SDAS reliability.
Comparative analysis The comparative chart Figure 11 shows that the SDAS effectively distinguishes noise patterns across varied environments.
The classroom recorded the highest variability, while the IoT Lab remained most stable.
For future testing, deployment in outdoor environments .
, highways and market.
will capture broader acoustic profiles.
Advanced techniques such as analysis of variance (ANOVA) and machine learning classification are planned for noise-source identification and predictive trend modeling.
The comparative analysis in Figure 11 highlights the SDASAos effectiveness in distinguishing acoustic profiles across multiple locations.
For future work, the research team plans to extend testing to outdoor environments, including traffic intersections and market zones, to capture complex urban noise signatures.
Statistical tools such as ANOVA and machine learning-based noise classification will also be incorporated to enhance predictive analysis and differentiate between noise sources .
, vehicles, machinery, and human These results confirm the SDAS effectively captures distinct acoustic behaviors in academic spaces and aligns with prior findings on indoor noise dynamics .
They also reinforce the value of AI-integrated systems for high-activity environments .
, the importance of ICT-based acoustic standards .
, and the relevance of distributed low-cost acoustic sensor networks for real-time urban monitoring .
Figure 11.
Comparative chart Summary table Table 1 consolidates mean, peak, and variability values for all locations.
Results show that the IoT lab maintained the lowest levels .
2 dB.
SD 1.
, while the classroom had the highest .
1 dB.
SD The graduate lab reached the maximum peak .
dB) and the embedded lab exhibited moderate fluctuations .
3 dB.
SD 4.
These findings confirm the SDAS accurately distinguishes between quiet and dynamic environments, supporting its deployment for smart campus and broader urban monitoring.
Table 1.
Summary of noise levels across test locations Location IoT lab Graduate lab Embedded lab Classroom Mean noise level .
B) Peak noise level .
B) Variability .
tandard deviation, dB) TELKOMNIKA Telecommun Comput El Control.
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April 2026: 599-607 TELKOMNIKA Telecommun Comput El Control Future applications and limitations While the system performed as intended across all locations, further refinements such as Wi-Fi data syncing, automated data classification, and multi-node deployments are recommended.
The current version assumes a single-node setup with manual deployment, which can be improved for long-term unattended Despite these limitations, the systemAos modular design, low cost, and reproducibility make it a strong candidate for scaling in similar institutions or urban setups concerned with environmental noise.
The results reinforce the research objectives and justify the value of a low-cost.
MATLAB-integrated SDAS for real-time environmental monitoring and analysis.
Testing and evaluation of the system The SDAS was tested in multiple campus locations, capturing noise fluctuations across both quiet and dynamic environments.
A panel of eight experts with masterAos degrees in computer applications evaluated its usability, integration with MATLAB, and reliability.
Overall feedback was positive, highlighting ease of deployment, accuracy of captured data, and clarity of visualizations.
Minor issues included a small learning curve for some users and the need for clearer documentation.
Despite this, most experts found the system intuitive, reliable, and suitable for regular use.
Figure 12 summarizes the evaluation results, confirming the SDASAos readiness for wider adoption in real-time noise monitoring.
Figure 12.
Expert evaluation CONCLUSION This study designed and implemented a low-cost ESP32-based SDAS for real-time monitoring of indoor noise levels.
The system integrates MEMS-based acoustic sensing.
ESP-WROOM-32 processing.
MATLAB visualization, and local data storage.
Testing across four campus locations produced mean noise levels of 14.
2 dB (IoT la.
, 27.
5 dB .
raduate la.
, 25.
3 dB .
mbedded la.
, and 32.
1 dB .
, confirming its accuracy and stability.
Limitations include single-node deployment, indoor-only validation, and USB-based MATLAB transmission.
Despite these, the SDAS demonstrates high usability and reliability as confirmed by expert evaluation.
Future research will focus on multi-node wireless synchronization, cloud integration, and AI-driven noise classification to enable large-scale smart city acoustic monitoring.
ACKNOWLEDGMENTS
The authors fully acknowledge the Mindanao State University.
Iligan of Technology.
Department of Computer Applications.
College of Computer Studies, and Engineering Research and Development for Technology.
FUNDING INFORMATION
This research received no external funding.
it was fully supported by the authorAos personal resources.
Low-cost ESP32-based sound data acquisition system with MATLAB A (Reymark-John A.
Macapana.
A ISSN: 1693-6930
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author Reymark-John A.
Macapanas Adrian P.
Galido Apple Rose B.
Alce C : Conceptualization M : Methodology So : Software Va : Validation Fo : Formal analysis ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue I : Investigation R : Resources D : Data Curation O : Writing - Original Draft E : Writing - Review & Editing ue ue Vi : Visualization Su : Supervision P : Project administration Fu : Funding acquisition CONFLICT OF INTEREST STATEMENT All authors declare that they have no conflict of interest.
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author.
RJM, upon reasonable request.
REFERENCES