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The Haar Wavelet Transform in IoT Digital Audio Signal Processing

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Abstract

Digital signal processing allows a wide range of digital applications, including audio processing. Discrete wavelet transform (DWT) is one of the most efficient ways of time–frequency analysis of an audio signal. The Internet of Things (IoT) is a technology that has been growing in commercial automation applications with the availability of low-cost modules and microcontrollers. IoT projects that use DWT for digital audio processing can solve many problems involving audio signals. In this paper, a performance comparison of an algorithm implementing DWT on three commercially available IoT devices is presented. The results show that it is possible to process signals in the 256 Hz to 6.5 KHz range, opening possibilities for future work integrating the technologies described.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. V.K. Abdrakhmanov, R.B. Salikhov, K.V. Vazhdacv, Development of a sound recognition system using stm32 microcontrollers for monitoring the state of biological objects, in 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE) (2018), pp. 170–173

  2. R. Amin, K. Shah, M. Asif, I. Khan, F. Ullah, An efficient algorithm for numerical solution of fractional integro-differential equations via Haar wavelet. J. Comput. Appl. Math. 381, 113028 (2021)

    Article  MathSciNet  Google Scholar 

  3. Atmel. Atmel atmega640/v-1280/v-1281/v-2560/v-2561/v, 2021. Accessed 2 July 2021

  4. D. Beladjine, D. Boudana, A. Moualdia, P. Wira, Wireless space vector pulse width modulation control of three-phase inverter using RF-link, in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (2020), pp. 1–6

  5. L.G. Caobianco, R.C. Guido, I.N. da Silva, Wavelet-based features selected with paraconsistent feature engineering successfully classify events in low-voltage grids. Measurement 170, 108711 (2021)

    Article  Google Scholar 

  6. I.B.F. de Almeida, L.L. Mendes, J.J.P.C. Rodrigues, M.A.A. da Cruz, 5G waveforms for IoT applications. IEEE Commun. Surv. Tutor. 21(3), 2554–2567 (2019)

    Article  Google Scholar 

  7. U.B. de Souza, J.P.L. Escola, L. da Cunha Brito, A survey on Hilbert–Huang transform: evolution, challenges and solutions. Digit. Signal Process. 120, 103292 (2022)

    Article  Google Scholar 

  8. L. Deng, D. O’Shaughnessy, Speech Processing: A Dynamic and Optimization-Oriented Approach (CRC Press, Boca Raton, 2018)

  9. A.K. Dezotti, A.M. Cardoso, L.E. Soares, D.H.B. Maccagnan et al., Device for monitoring population density of insects from acoustic signals emitted. Braz. J. Anim. Environ. Res. 2(5), 1781–1785 (2019)

    Google Scholar 

  10. J.P.L. Escola, T.A. Docusse, Serialização de dados em processamento digital de sinais: um estudo de caso. Revista Tecnologia 41, 2 (2020)

    Article  Google Scholar 

  11. J.P.L. Escola, R.C. Guido, A.M. Cardoso, D.H.B. Maccagnan, J.M. Ribeiro, J.R.F. Cardoso, A case study of wavelets and SVM application in coffee agriculture: detecting cicadas based on their acoustic and image patterns, in Application of Expert Systems. ed. by I.N. da Silva, R.A. Flauzino (IntechOpen, Rijeka, 2020), chap. chap. 1

    Google Scholar 

  12. J.P.L. Escola, R.C. Guido, I.N. da Silva, A.M. Cardoso, D.H.B. Maccagnan, A.K. Dezotti, Automated acoustic detection of a cicadid pest in coffee plantations. Comput. Electron. Agric. 169, 105215 (2020)

    Article  Google Scholar 

  13. J.P.L. Escola, R.C. Guido, I.N. da Silva, L.E. Soares, IoT device coverage analysis for smart farm monitoring. RISTI (Revista Iberica de Sistemas e Tecnologias de Informacao) 42, 1–12 (2021)

    Google Scholar 

  14. Espressif, Heap memory allocation (2021). Accessed 2 June 2021

  15. M. Frustaci, P. Pace, G. Aloi, G. Fortino, Evaluating critical security issues of the IoT world: present and future challenges. IEEE Internet Things J. 5(4), 2483–2495 (2018)

    Article  Google Scholar 

  16. I. Grokhotkov, Esp8266 arduino core documentation (2017). Accessed 20 Dec 2021

  17. R.C. Guido, Effectively interpreting discrete wavelet transformed signals [lecture notes]. IEEE Signal Process. Mag. 34(3), 89–100 (2017)

    Article  MathSciNet  Google Scholar 

  18. R.C. Guido, Paraconsistent feature engineering [lecture notes]. IEEE Signal Process. Mag. 36(1), 154–158 (2018)

    Article  Google Scholar 

  19. N.K.S. Gupta, S. Shukla Datta, Wavelet based real-time monitoring of electrical signals in distributed generation (DG) integrated system. Eng. Sci. Technol. Int. J. 24(1), 218–228 (2021)

    Google Scholar 

  20. S.S. Haykin, B. Van Veen, Signals and Systems (Wiley, Hoboken, 2007)

    MATH  Google Scholar 

  21. L. Q. Huy, N. Duc Hung,T. P. Hoa, N. Dinh Tuyen, Control and monitor of single-stage single-phase t-type grid-connected inverter based on IoT, in 2021 International Conference on System Science and Engineering (ICSSE) (2021), pp. 231–236

  22. A. Khajuria, D. Joshi, Effects of vibrotactile feedback on postural sway in trans-femoral amputees: a wavelet analysis. J. Biomech. 115, 110145 (2021)

    Article  Google Scholar 

  23. S. Kumar, R. Kumar, R.P. Agarwal, B. Samet, A study of fractional Lotka–Volterra population model using Haar wavelet and Adams–Bashforth–moulton methods. Math. Methods Appl. Sci. 43(8), 5564–5578 (2020)

    Article  MathSciNet  Google Scholar 

  24. A. Kurniawan, Internet of Things Projects with ESP32: Build Exciting and Powerful IoT Projects Using the All-New Espressif ESP32 (Packt Publishing Ltd, Birmingham, 2019)

    Google Scholar 

  25. J. Kwak, S. Kim, S. Lee, J. Cho, D. Park, Energy-efficient ECG event signal processing using primitive-based QRS complex detection, in 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) (2018), pp. 215–216

  26. B.P. Lathi, R.A. Green, Linear Systems and Signals, vol. 2 (Oxford University Press, New York, 2005)

    Google Scholar 

  27. S. Mallik, D. Chowdhury, M. Chttopadhyay, Development and performance analysis of a low-cost mems microphone-based hearing aid with three different audio amplifiers. Innov. Syst. Softw. Eng. 15(1), 17–25 (2019)

    Article  Google Scholar 

  28. M. McRoberts, Beginning Arduino (Apress, New York, 2011)

    Google Scholar 

  29. R. Michon,Y. Orlarey, S. Letz, D. Fober, The Faust programming language as a platform for creating hybrid acoustical and digital musical instruments, in Forum Acusticum 2020 (FA 2020) (Lyon, France, Dec), Forum Acusticum 2020 Proceedings (2020)

  30. R. Mitev, A. Pazii, M. Miettinen, W. Enck, A.-R. Sadeghi, Leakypick: Iot audio spy detector, in Annual Computer Security Applications Conference (2020), pp. 694–705

  31. S. Monk, Programming Arduino: Getting Started with Sketches (McGraw-Hill Education, New York, 2016)

    Google Scholar 

  32. R. Morabito, V. Cozzolino, A.Y. Ding, N. Beijar, J. Ott, Consolidate IoT edge computing with lightweight virtualization. IEEE Netw. 32(1), 102–111 (2018)

    Article  Google Scholar 

  33. J. Nebhen, P.M. Ferreira, S. Mansouri, A chopper stabilization audio instrumentation amplifier for IoT applications. J. Low Power Electron. Appl. 10(2), 13 (2020)

    Article  Google Scholar 

  34. D. Rahmawati, H. Haryanto, F. Sakariya, The design of coconut maturity prediction device with acoustic frequency detection using Naive Bayes method based microcontroller. JEEMECS J. Electr. Eng. Mechatron. Comput. Sci. 2, 15–20 (2019)

    Google Scholar 

  35. A.C. Ramírez, F. Moumtadi, Design of a hearing auxiliary for bilateral hypoacusia. Int. J. Eng. Sci. Res. Technol. (2018). https://doi.org/10.5281/zenodo

    Article  Google Scholar 

  36. S. Rezwan, W. Ahmed, M.A. Mahia, M.R. Islam, Iot based smart inventory management system for kitchen using weight sensors, ldr, led, arduino mega and nodemcu (esp8266) wi-module with website and app (2018)

  37. E. Rochester, J. Ma, B. Lee, M. Ghaderi, Mountain pine beetle monitoring with IoT, in 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) (IEEE, 2019), pp. 513–518

  38. F. Samie, V. Tsoutsouras, L. Bauer, S. Xydis, D. Soudris, J. Henkel, Computation offloading and resource allocation for low-power IoT edge devices, in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) (2016), pp. 7–12

  39. M. Schwartz, Internet of Things with ESP8266 (Packt Publishing Ltd, Birmingham, 2016)

    Google Scholar 

  40. S.K. Shah, Z. Tariq, Y. Lee, Audio IoT analytics for home automation safety, in 2018 IEEE International Conference on Big Data (Big Data) (IEEE, 2018), pp. 5181–5186

  41. M. Shen, B. Ma, L. Zhu, X. Du, K. Xu, Secure phrase search for intelligent processing of encrypted data in cloud-based IoT. IEEE Internet Things J. 6(2), 1998–2008 (2019)

    Article  Google Scholar 

  42. Z. Yu, X. Zheng, F. Huang, W. Guo, L. Sun, Z. Yu, A framework based on sparse representation model for time series prediction in smart city. Front. Comput. Sci. 15(1), 1–13 (2021)

    Article  Google Scholar 

  43. L. Zada, I. Aziz, Numerical solution of fractional partial differential equations via Haar wavelet, in Numerical Methods for Partial Differential Equations (2020)

  44. A. Zgank, Bee swarm activity acoustic classification for an IoT-based farm service. Sensors 20(1), 21 (2020)

    Article  Google Scholar 

  45. M. Zhang, M. Sun, P. Lai, Z. Zhu, Distributed environmental parameter measurement system based on tms320f28335, in 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (2019), pp. 1–6

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Acknowledgements

We gratefully acknowledge the grants provided by the Brazilian agencies “National Council for Scientific and Technological Development (CNPq)” and “The State of São Paulo Research Foundation (FAPESP),” respectively, through the processes 306808/2018-8 and 2019/04475-0, in support of this research.

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Authors and Affiliations

  1. Instituto Federal de São Paulo, Av. C1 n250, Barretos, SP, 14781-502, Brazil

    João Paulo Lemos Escola

  2. Instituto Federal de Goiás, Rua 75 n46, Goiânia, GO, 74055-110, Brazil

    Uender Barbosa de Souza

  3. Instituto de Biociências, Letras e Ciências Exatas, Unesp - Universidade Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo n2265, São José do Rio Preto, SP, 15054-000, Brazil

    Rodrigo Capobianco Guido

  4. Escola de Engenharia de São Carlos, Universidade de São Paulo, Av. Trabalhador São-carlense n400, São Carlos, SP, 13566-590, Brazil

    João Paulo Lemos Escola & Ivan Nunes da Silva

  5. EMC, Universidade Federal de Goiás, Av. Universitária n1488, Goiânia, GO, 74605-010, Brazil

    Uender Barbosa de Souza

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  1. João Paulo Lemos Escola
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  2. Uender Barbosa de Souza
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  3. Rodrigo Capobianco Guido
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  4. Ivan Nunes da Silva
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Correspondence to João Paulo Lemos Escola.

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Escola, J.P.L., de Souza, U.B., Guido, R.C. et al. The Haar Wavelet Transform in IoT Digital Audio Signal Processing. Circuits Syst Signal Process 41, 4174–4184 (2022). https://doi.org/10.1007/s00034-022-01979-8

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  • DOI: https://doi.org/10.1007/s00034-022-01979-8

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