A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%; thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
KeywordsEmergency ResponseEmergency Management TeamAudio Signal AlertingAutomatic Control SystemUni Pro XLManual CommunicationFast Fourier Transform MagnitudeZero Crossing RateRoot Means Square
Can, A.B., et al. (2022) A New Method of Automatic Content Analysis in Disaster Management. 2022 10th International Symposium on Digital Forensics and Security (ISDFS), Istanbul, 6-7 June 2022, 1-5.
Van Der Meer, T.G. (2016) Automated Content Analysis and Crisis Communication Research. Public Relations Review, 42, 952-961. https://doi.org/10.1016/j.pubrev.2016.09.001
Liang, J., et al. (2015) Detecting Semantic Concepts in Consumer Videos Using Audio. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 2, 2279-2283.
Zhang, J. (2021) Music Feature Extraction and Classification Algorithm Based on Deep Learning. Scientific Programming, 2021, Article ID: 1651560. https://doi.org/10.1155/2021/1651560
Laurence, D. (2005) Safety Rules and Regulations on Mine Sites—The Problem and a Solution. Journal of Safety Research, 36, 39-50. https://doi.org/10.1016/j.jsr.2004.11.004
Topp, K., et al. (2018) Anatomical Society Summer Meeting in Galway.
Sujyothi, P. (2014) Software Test Rig Development and Testing Sequences for Genset Paralleling System: A Research Report. Doctoral Dissertation, Vellore Institute of Technology, Vellore.
Michael, I.A. (2022) Design of Lift Group Control Systems Based on PLC. Journal of Engineering Research and Reports, 22, 31-44. https://doi.org/10.9734/jerr/2022/v22i317528
Porter, E. (2005) Wearing and Using Personal Emergency. Journal of Gerontological Nursing, 31, 26-33. https://doi.org/10.3928/0098-9134-20051001-07
Sixsmith, A. (2004) A Smart Sensor to Detect the Falls of the Elderly. IEEE Pervasive Computing, 3, 42-47. https://doi.org/10.1109/MPRV.2004.1316817
Mann, W. (2005) Use of Personal Emergency Response Systems by Older Individuals with Disabilities. Assistive Technology, 17, 82-88. https://doi.org/10.1080/10400435.2005.10132098
Hamill, M. (2018) Development of an Automated Speech Recognition Interface for Personal Emergency Response Systems. Journal of NeuroEngineering and Rehabilitation, 6, Article No. 26. https://doi.org/10.1186/1743-0003-6-26
Govender, D. (2018) Investigating Audio Classification to Automate the Trimming of Recorded Lectures. University of Cape Town, Cape Town.
Grosche (2018) Audio Content-Based Music Retrieval. Artificial Intelligence Research Institute (IIIA-CSIC), Campus UAB, Barcelona.
Dong (2016) Design of Wireless Automatic Fire Alarm System. Procedia Engineering, 135, 413-417. https://doi.org/10.1016/j.proeng.2016.01.149
Lakshmana, P. (2018) Efficient Smart Emergency Response System for Fire Hazards Using IoT. International Journal of Advanced Computer Science and Applications, 9, 314-320. https://doi.org/10.14569/IJACSA.2018.090143
Félix-Brasdefer, J.C. (2010) Data Collection Methods in Speech Act Performance. In: Martínez-Flor, A. and Usó-Juan, E., Eds., Speech Act Performance: Theoretical, Empirical and Methodological Issues, John Benjamins Publishing Company, Amsterdam, 69-82. https://doi.org/10.1075/lllt.26.03fel
Wang, Z., et al. (2020) Data Pre-Processing Methods for Electrical Impedance Tomography: A Review. Physiological Measurement, 41, 2-9. https://doi.org/10.1088/1361-6579/abb142
Majidi, M., et al. (2015) Improving Pattern Recognition Accuracy of Partial Discharges by New Data Pre-Processing Methods. Electric Power Systems Research, 119, 100-110. https://doi.org/10.1016/j.epsr.2014.09.014
Alasadi, S.A. and Bhaya, W.S. (2017) Review of Data Preprocessing Techniques in Data Mining. Journal of Engineering and Applied Sciences, 12, 4102-4107.
Danubianu, M. (2015) Step-by-Step Data Pre-Processing for Data Mining. A Case Study. Proceedings of the International Conference on Information Technologies (InfoTech-2015), Varna, 17-18 September 2020, 117-124.
Chicco, D. (2017) Ten Quick Tips for Machine Learning in Computational Biology. Biodata Mining, 10, Article No. 35. https://doi.org/10.1186/s13040-017-0155-3
Choi, K., et al. (2018) A Comparison of Audio Signal Pre-Processing Methods for Deep Neural Networks on Music Tagging. 2018 26th IEEE European Signal Processing Conference (EUSIPCO), Rome, 3-7 September 2018, 1870-1874. https://doi.org/10.23919/EUSIPCO.2018.8553106
McLoughlin, I.V. Speech and Audio Processing: A MATLAB-Based Approach. Cambridge University Press, Cambridge.
Greener, J.G., et al. (2022) A Guide to Machine Learning for Biologists. Nature Reviews Molecular Cell Biology, 23, 40-55. https://doi.org/10.1038/s41580-021-00407-0
Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A. and Arshad, H. (2018) State-of-the-Art in Artificial Neural Network Applications: A Survey. Heliyon, 4, e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Moffat, D., Ronan, D. and Reiss, J.D. (2015) An Evaluation of Audio Feature Extraction Toolboxes.
Tate, M. (2013) Principles of Hearing and Audiology. 283.
Piersol, A.G. and Paez, T.L. (2010) Harris’ Shock and Vibration Handbook. McGraw-Hill Education, London.
Addor, J.A., Wiah, E.N. and Alao, F.I. (2022) An Improved Two-States Cyclical Dynamic Model for Plastic Waste Management, Asian Research Journal of Mathematics, 18, 52-68. https://doi.org/10.9734/arjom/2022/v18i530378
Slabbekoorn, H., Bouton, N, van Opzeeland, I., Coers, A., ten Cate, C. and Popper, A.N. (2010) A Noisy Spring: The Impact of Globally Rising Underwater Sound Levels on Fish. Trends in Ecology & Evolution, 25, 419-427. https://doi.org/10.1016/j.tree.2010.04.005
Takahashi, D. (2019). Fast Fourier Transform Algorithms for Parallel Computers. Springer, Singapore. https://doi.org/10.1007/978-981-13-9965-7
Joo, S., Choi, J., Kim, N. and Lee, M.C. (2021) Zero-Crossing Rate Method as an Efficient Tool for Combustion Instability Diagnosis. Experimental Thermal and Fluid Science, 123, Article ID: 110340. https://doi.org/10.1016/j.expthermflusci.2020.110340
Room, C. (2021) Audio Feature Extraction. Machine Learning, 16, 51.
Zaw, T.H. and War, N. (2017) The Combination of Spectral Entropy, Zero Crossing Rate, Short Time Energy and Linear Prediction Error for Voice Activity Detection. 2017 20th IEEE International Conference of Computer and Information Technology (ICCIT), Dhaka, 22-24 December 2017, 1-5. https://doi.org/10.1109/ICCITECHN.2017.8281794
Sharm, A.G., Umapathy, K. and Krishnan, S. (2020) Trends in Audio Signal Feature Extraction Methods. Applied Acoustics, 158, Article ID: 107020. https://doi.org/10.1016/j.apacoust.2019.107020
Martin-Morato, I., Cobos, M. and Ferri, F.J. (2018) Adaptive Mid-Term Representations for Robust Audio Event Classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26, 2381-2392. https://doi.org/10.1109/TASLP.2018.2865615
Saddam, S.A.W. (2022) Wind Sounds Classification Using Different Audio Feature Extraction Techniques. Informatica, 45, 57-65. https://doi.org/10.31449/inf.v45i7.3739
Wu, J., Xu, Y., Zhang, S.X., Chen, L.W., Yu, M., Xie, L. and Yu, D. (2019) Time Domain Audio Visual Speech Separation. 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Sentosa, 14-18 December 2019, 667-673. https://doi.org/10.1109/ASRU46091.2019.9003983
Bartusiak, E.R. and Delp, E.J. (2022) Frequency Domain-Based Detection of Generated Audio.
Fang, Y., Liu, D., Jiang, Z. and Wang, H. (2023) Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare. Journal of Healthcare Engineering, 2023, Article ID: 6197564. https://doi.org/10.1155/2023/6197564
Ayvaz, U., Gürüler, H., Khan, F., Ahmed, N., Whangbo, T. and Bobomirzaevich, A. (2022) Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients through Machine Learning. CMC-Computers Materials & Continua, 71, 5511-5521. https://doi.org/10.32604/cmc.2022.023278