Sustainable Live Sound Monitoring and Classification System Enabled by a Triboelectric Nanogenerator and Machine Learning Techniques

Abstract

The growing demand for sustainable, real-time audio processing drives innovations in sound classification and energy harvesting. Traditional sound monitoring systems often struggle with scalability, energy efficiency, and adaptability, particularly in remote or resource-limited environments. The expansion of IoT applications intensifies power demands in widely distributed wireless sensor networks, highlighting the need for sustainable solutions. Moreover, the volume of data generated by these sensors frequently exceeds the capacity for efficient human analysis, necessitating the integration of machine learning and deep learning techniques. These methods must be optimized for fine-tuning with minimal data from new sensors, enabling efficient and accurate sound classification without extensive retraining. This paper presents a Triboelectric Nanogenerator (TENG)-based microphone that addresses energy consumption and data processing challenges by integrating advanced materials with sound classification systems. The proposed device uses polyimine/graphite polypropylene (PI/GP) coated paper to capture sound and harvest energy from ambient noise. It delivers an output power of 25.67 μW at 94 dB, powering a wireless transmission circuit while achieving high acoustic sensitivity and a frequency response of up to 20 kHz. Performance evaluations show 92.7% classification accuracy in simulated live environments and a processing time of 0.342 s for 5-s audio clips using the MobileNet V1 model. Pre-trained models fine-tuned with minimal data from the TENG microphone enable efficient sound classification without extensive retraining. This innovation offers a sustainable alternative to conventional microphones, supporting self-powered, real-time monitoring systems with wireless data transmission and energy storage capabilities.

Keywords

deep learning / energy-harvesting / live sound monitoring / self-powered microphone / triboelectric nanogenerator

References

[1] Y. Yang, Y. Kartynnik, Y. Li, J. Tang, X. Li, G. Sung, M. Grundmann, STREAMVC: Real-Time Low-Latency Voice Conversion, 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, 2024.

[2] F. Ahsan, N. H. Dana, S. K. Sarker, L. Li, S. M. Muyeen, M. F. Ali, Z. Tasneem, M. M. Hasan, S. H. Abhi, M. R. Islam, M. H. Ahamed, M. M. Islam, S. K. Das, M. F. R. Badal, P. Das, Prot. Control Mod. Power Syst. 2023, 8, 43.

[3] F. K. Donkor, K. Mearns, in Affordable and Clean Energy (Eds: W. Leal Filho, A. M. Azul, L. Brandli, A. Lange Salvia, T. Wall), Springer International Publishing, Cham 2020, pp. 1–9.

[4] R. Raman, D. Pattnaik, H. H. Lathabai, C. Kumar, K. Govindan, P. Nedungadi, J. Big Data 2024, 11, 55.

[5] A. van Wynsberghe, AI Ethics 2021, 1, 213.

[6] K. R. Kaja, S. Hajra, S. Panda, M. A. Belal, P. Pakawanit, N. Vittayakorn, C. Bowen, H. Khanbareh, H. J. Kim, Adv. Sustain. Syst. 2024,

[7] O. O. Abayomi-Alli, R. Damaševičius, A. Qazi, M. Adedoyin-Olowe, S. Misra, Electronics 2022, 11, 3795.

[8] J. Yang, K. Hong, Y. Hao, X. Zhu, Y. Qin, W. Su, H. Zhang, C. Zhang, Z. L. Wang, X. Li, Adv. Mater. Technol. 2025, 10, 2400554.

[9] R. K. Rajaboina, U. K. Khanapuram, A. Kulandaivel, Adv. Sensor Res. 2024, 3, 2400045.

[10] A.-L. Georgescu, A. Pappalardo, H. Cucu, M. Blott, Eurasip J. Audio Speech Music Process. 2021, 2021, 28.

[11] A. M. Tripathi, A. Mishra, Appl. Acoust. 2021, 182, 108183.

[12] Z. Lin, S. Duan, M. Liu, C. Dang, S. Qian, L. Zhang, H. Wang, W. Yan, M. Zhu, Adv. Mater. 2024, 36, 2306880.

[13] M. H. Bagheri, J. Li, E. Gu, K. Habashy, M. M. Rana, A. A. Khan, Y. Zhang, G. Xiao, P. Xi, D. Ban, Adv. Sensor Res. 2024, 4, 2400156.

[14] M. Chi, S. Chen, J. Jiao, N. Yu, J. Appl. Phys. 2023, 133, 245105.

[15] M. A. Pillai, E. Deenadayalan, Int. J. Precis. Eng. Manuf. 2014, 15, 949.

[16] G. Zhu, Y. Zhou, Z. Si, Y. Cheng, F. Wu, H. Wang, Y. Pan, J. Xie, C. Li, A. Chen, R. Wang, J. Sun, Nano Energy 2023, 108, 108237.

[17] R. Tabassian, A. Rajabi-Abhari, M. Mahato, H. Yoo, H. Y. Yoon, J. Y. Park, I.-K. Oh, SmartMat 2024, 5, e1270.

[18] K. Dong, X. Peng, J. An, A. C. Wang, J. Luo, B. Sun, J. Wang, Z. L. Wang, Nat. Commun. 2020, 11, 2868.

[19] H. Yang, J. A. Lai, Q. Li, X. Zhang, X. Li, Q. Yang, Y. Hu, Y. Xi, Z. L. Wang, Nano Energy 2022, 104, 107932.

[20] H. Guo, X. Pu, J. Chen, Y. Meng, M.-H. Yeh, G. Liu, Q. Tang, B. Chen, D. Liu, S. Qi, C. Wu, C. Hu, J. Wang, Z. L. Wang, Sci. Robot. 2018, 3, eaat2516.

[21] T. H. Le, T. Van Le, V.-T. Bui, C. C. Nguyen, M. T. N. Dinh, N. M. Chau, V.-T. Bui, Nano Energy 2024, 129, 109963.

[22] N. R. Tanguy, M. Rana, A. A. Khan, X. Zhang, N. Tratnik, H. Chen, D. Ban, N. Yan, Nano Energy 2022, 98, 107337.

[23] X. Pu, C. Zhang, Z. L. Wang, Natl. Sci. Rev. 2022, 10, nwac170.

[24] J. Wang, Y. Zi, S. Li, X. Chen, MRS Energy Sustain. 2020, 6, 17.

[25] A. Chang, C. Uy, X. Xiao, X. Xiao, J. Chen, Nano Energy 2022, 98, 107282.

[26] S. A. Zawawi, A. A. Hamzah, B. Y. Majlis, F. Mohd-Yasin, Micromachines 2020, 11, 484.

[27] D. Jiang, M. Lian, M. Xu, Q. Sun, B. B. Xu, H. K. Thabet, S. M. El-Bahy, M. M. Ibrahim, M. Huang, Z. Guo, Adv. Compos. Hybrid Mater. 2023, 6, 57.

[28] Y. Li, J. Yu, Y. Wei, Y. Wang, Z. Feng, L. Cheng, Z. Huo, Y. Lei, Q. Sun, Sensors 2023, 23, 1329.

[29] J. Jo, S. Panda, N. Kim, S. Hajra, S. Hwang, H. Song, J. Shukla, B. K. Panigrahi, V. Vivekananthan, J. Kim, P. G. Raju Achary, H. Keum, H. Kim, J. Sci. Adv. Mater. Devices 2024, 9, 100693.

[30] S. Zhou, C. Jia, G. Shu, Z. Guan, H. Wu, J. Li, W. Ou-Yang, Nano Energy 2024, 129, 109951.

[31] J. Hu, M. Iwamoto, X. Chen, Nano-Micro Lett. 2023, 16, 7.

[32] A. Babu, S. Gupta, R. Katru, N. Madathil, A. Kulandaivel, P. Kodali, H. Divi, H. Borkar, U. K. Khanapuram, R. K. Rajaboina, Energ. Technol. 2024, 12, 2400796.

[33] S. Paria, S. K. Si, S. K. Karan, A. K. Das, A. Maitra, R. Bera, L. Halder, A. Bera, A. De, B. B. Khatua, J. Mater. Chem. A 2019, 7, 3979.

[34] H. Zhang, X. Gong, X. Li, J. Mater. Chem. A 2023, 11, 24454.

[35] A. Panda, K. K. Das, K. R. Kaja, M. Belal, B. K. Panigrahi, J. Met. Mater. Miner. 2024, 34, 2170.

[36] A. Rajabi-Abhari, P. Li, M. H. Bagheri, A. A. Khan, C. Hao, N. R. Tanguy, D. Ban, L. Yu, N. Yan, Nano Energy 2024, 131, 110306.

[37] W. Sun, J. Chen, T. Yuan, D. Sui, J. Zhou, Nano Energy 2024, 128, 109913.

[38] A. Babu, P. Malik, N. Das, D. Mandal, Small 2022, 18, 2201331.

[39] X. Tao, X. Chen, Z. L. Wang, Energy Environ. Sci. 2023, 16, 3654.

[40] Y. S. Choi, S.-W. Kim, S. Kar-Narayan, Adv. Energy Mater. 2021, 11, 2003802.

[41] R. S. Puppala, K. Prakash, R. R. Kumar, M. F. Hashmi, K. U. Kumar, 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS). Nagpur, India, 2023, pp. 1–5.

[42] R. Zhang, Cell Rep. Phys. Sci. 2024, 5, 101888.

[43] A. O. Albaji, R. B. A. Rashid, S. Z. Abdul Hamid, J. Electr. Comput. Eng. 2023, 2023, 3615137.

[44] S. Bhattacharya, N. Das, S. Sahu, A. Mondal, S. Borah, in Lecture Notes in Networks and Systems (Eds: V.H. Patil, N. Dey, P.N. Mahalle, M. Shafi Pathan, V.V. Kimbahune), Springer, Singapore 2021.

[45] S. Chu, S. Narayanan, C. C. J. Kuo, IEEE Trans. Audio Speech Lang. Process. 2009, 17, 1142.

[46] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, arXiv. preprint arXiv:1704.04861 2017,

[47] K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016.

[48] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the Inception Architecture for Computer Vision, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016.

[49] F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017.

[50] X. Liu, Z. Jia, X. Hou, M. Fu, L. Ma, Q. Sun, Real-time Marine Animal Images Classification by Embedded System Based on Mobilenet and Transfer Learning, OCEANS 2019 – Marseille, Marseille, France, 2019.

[51] L. Gang, Z. Haixuan, E. Linning, Z. Ling, L. Yu, Z. Juming, Med. Phys. 2021, 48, 4304.

[52] K. J. Piczak, ESC: Dataset for Environmental Sound Classification, Proceedings of the 23rd ACM international conference on Multimedia (MM '15), New York, NY, USA, 2015.

[53] D. Ranmal, P. Ranasinghe, T. Paranayapa, Sensors 2024, 24, 3749.

[54] G. Jekateryńczuk, Z. Piotrowski, Sensors 2023, 24, 68.

[55] L. Chen, Q. Shi, Y. Sun, T. Nguyen, C. Lee, S. Soh, Adv. Mater. 2018, 30, 1802405.

[56] S. Wang, Y. Zi, Y. S. Zhou, S. Li, F. Fan, L. Lin, Z. L. Wang, J. Mater. Chem. A 2016, 4, 3728.

[57] Y. Tang, B. Xu, Y. Gao, Z. Li, D. Tan, M. Li, Y. Liu, J. Huang, Nano Energy 2022, 103, 107833.

[58] G. M. Rani, C.-M. Wu, K. G. Motora, R. Umapathi, C. R. M. Jose, Nano Energy 2023, 108, 108211.

[59] S. Kuntharin, V. Harnchana, A. Klamchuen, K. Sinthiptharakoon, P. Thongbai, V. Amornkitbamrung, P. Chindaprasirt, ACS Sustain. Chem. Eng. 2022, 10, 4588.

[60] A. Rajabi-Abhari, J.-N. Kim, J. Lee, R. Tabassian, M. Mahato, H. J. Youn, H. Lee, I.-K. Oh, ACS Appl. Mater. Interfaces 2021, 13, 219.

[61] K. R. Kaja, S. Hajra, S. Panda, M. A. Belal, U. Pharino, H. Khanbareh, N. Vittayakorn, V. Vivekananthan, C. Bowen, H. J. Kim, Nano Energy 2024, 131, 110319.

[62] A. Chen, C. Zhang, G. Zhu, Z. L. Wang, Adv. Sci. 2020, 7, 2000186.

[63] W. Sun, Z. Jiang, X. Xu, Q. Han, F. Chu, Int. J. Non Linear Mech. 2021, 136, 103773.

[64] N. Arora, T. Starner, G. D. Abowd, Commun. ACM 2020, 63, 12.

[65] H. Shao, H. Wang, Y. Cao, X. Ding, J. Fang, H. Niu, W. Wang, C. Lang, T. Lin, Nano Energy 2020, 75, 104956.

[66] S. Chai, X. Liu, X. Wu, Y. Xiong, Sensors 2021, 21, 279.

[67] J. Wang, H. Liu, S. Han, G. Sun, X. Hu, Appl. Acoust. 2025, 227, 110258.

[68] J. Hillenbrand, S. Haberzettl, G. M. Sessler, J. Acoust. Soc. Am. 2013, 134, EL499.

[69] M. Qu, X. Chen, D. Yang, D. Li, K. Zhu, X. Guo, J. Xie, J. Micromech. Microeng. 2022, 32, 014001.

[70] Y. Li, C. Liu, S. Hu, P. Sun, L. Fang, S. Lazarouk, V. Labunov, W. Yang, D. Li, K. Fan, G. Wang, L. Dong, L. Che, Acoust. Aust. 2022, 50, 383.

[71] X. Hui, L. Tang, D. Zhang, S. Yan, D. Li, J. Chen, F. Wu, Z. L. Wang, H. Guo, Adv. Mater. 2024, 36, 2401508.

[72] J. H. Han, J.-H. Kwak, D. J. Joe, S. K. Hong, H. S. Wang, J. H. Park, S. Hur, K. J. Lee, Nano Energy 2018, 53, 198.

[73] G. Von Békésy, Nature 1970, 225, 1207.

[74] H. S. Lee, J. Chung, G.-T. Hwang, C. K. Jeong, Y. Jung, J.-H. Kwak, H. Kang, M. Byun, W. D. Kim, S. Hur, S.-H. Oh, K. J. Lee, Adv. Funct. Mater. 2014, 24, 6914.

[75] J. Li, R. Li, X. Ruan, IET Renew. Power Generation 2023, 17, 1106.

[76] X. He, H. Zhang, J. Jiang, X. Liu, Nanotechnology 2023, 34, 155403.

[77] Y. Zhang, T. Hu, R. Hu, S. Jiang, C. Zhang, H. Hou, Molecules 2022, 27, 8896.

[78] J. Yun, J. Park, M. Ryoo, N. Kitchamsetti, T. S. Goh, D. Kim, Nano Energy 2023, 105, 108018.

[79] K. Lee, H. Han, J. H. Ryu, S. Kang, K. Jung, Y.-K. Kim, T. Song, S. Mhin, K. M. Kim, Carbon 2023, 212, 118120.

[80] S. Niu, S. Wang, L. Lin, Y. Liu, Y. S. Zhou, Y. Hu, Z. L. Wang, Energy Environ. Sci. 2013, 6, 3576.

[81] H. Sun, X. Gao, L.-Y. Guo, L.-Q. Tao, Z. H. Guo, Y. Shao, T. Cui, Y. Yang, X. Pu, T.-L. Ren, InfoMat 2023, 5, e12385.

[82] X. Yu, Y. Shang, L. Zheng, K. Wang, ACS Appl. Electron. Mater. 2023, 5, 5240.

[83] F. Jean, M. U. Khan, A. Alazzam, B. Mohammad, J. Sci. Adv. Mater. Devices 2024, 9, 100805.

[84] Y. Kurihara, T. Kaburagi, K. Watanabe, IEEE Sensors J. 2016, 16, 1772.

[85] Y. Kurihara, T. Kaburagi, K. Watanabe, IEEE Sensors J. 2015,

[86] D. Theng, K. K. Bhoyar, Knowl. Inf. Syst. 2024, 66, 1575.

[87] W.-G. Kim, D.-W. Kim, I.-W. Tcho, J.-K. Kim, M.-S. Kim, Y.-K. Choi, ACS Nano 2021, 15, 258.

[88] M. H. Bagheri, A. A. Khan, S. Shahzadi, M. M. Rana, M. S. Hasan, D. Ban, Nano Energy 2024, 120, 109101.

[89] A. A. Khan, R. Saritas, M. M. Rana, N. Tanguy, W. Zhu, N. Mei, S. Kokilathasan, S. Rassel, Z. Leonenko, N. Yan, E. Abdel-Rahman, D. Ban, ACS Appl. Mater. Interfaces 2022, 14, 4119.

[90] M. M. Rana, A. A. Khan, W. Zhu, M. F. A. Fattah, S. Kokilathasan, S. Rassel, R. Bernard, S. Ababou-Girard, P. Turban, S. Xu, C. Wang, D. Ban, Nano Energy 2022, 101, 107631.

[91] M. M. Rana, A. A. Khan, G. Huang, N. Mei, R. Saritas, B. Wen, S. Zhang, P. Voss, E. Abdel-Rahman, Z. Leonenko, S. Islam, D. Ban, ACS Appl. Mater. Interfaces 2020, 12, 47503.

[92] M. Lamrini, M. Y. Chkouri, A. Touhafi, Sensors 2023, 23, 6227.

[93] B. da Silva, A. W. Happi, A. Braeken, A. Touhafi, Appl. Sci. 2019, 9, 3885.

[94] R. M. Alsina-Pagès, J. Navarro, F. Alías, M. Hervás, Sensors 2017, 17, 854.

[95] S. Niu, Z. L. Wang, Nano Energy 2015, 14, 161.

[96] R. D. I. G. Dharmasena, J. H. B. Deane, S. R. P. Silva, Adv. Energy Mater. 2018, 8, 1802190.

[97] H. J. Hwang, D. Choi, Funct. Compos. Struct. 2021, 3, 025004.

[98] S. Chao, H. Ouyang, D. Jiang, Y. Fan, Z. Li, EcoMat 2021, 3, e12072.

RIGHTS & PERMISSIONS

2025 The Author(s). Energy & Environmental Materials published by John Wiley & Sons Australia, Ltd on behalf of Zhengzhou University.