The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Machine learning-based self-powered acoustic sensor for speaker recognition
Abstract
Abstract Herein, we report a new platform of machine learning-based speaker recognition via the flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant frequency band. The resonant self-powered f-PAS was fabricated by mimicking the operating mechanism of the basilar membrane in the human cochlear. The f-PAS acquired abundant voice information from the multi-channel sound inputs. The standard TIDIGITS dataset were recorded by the f-PAS and converted to frequency components by using a Fast Fourier Transform (FFT) and a Short-Time Fourier Transform (STFT). The machine learning based Gaussian Mixture Model (GMM) was designed by utilizing the most highest and second highest sensitivity data among multi-channel outputs, exhibiting outstanding speaker recognition rate of 97.5% with error rate reduction of 75% compared to that of the reference MEMS microphone.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Who “Is Saying What”? Brain-based decoding of human voice and speech
Formisano, E., De Martino, F., Bonte, M., & Goebel, R. (2008) · Science · doi:10.1126/science.1164318 · Cited by 599
In Vivo Self‐Powered Wireless Transmission Using Biocompatible Flexible Energy Harvesters
Kim, D. H., Shin, H. J., Lee, H., Jeong, C. K., Park, H., Hwang, G., Lee, H., Joe, D. J., Han, J. H., Lee, S. H., et al. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201700341 · Cited by 245
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Cited by
Wen, F., Sun, Z., He, T., Shi, Q., Zhu, M., Zhang, Z., Li, L., Zhang, T., & Lee, C. (2020) · Advanced Science · doi:10.1002/advs.202000261 · Cited by 478
Khoshmanesh, F., Thurgood, P., Pirogova, E., Nahavandi, S., & Baratchi, S. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112946 · Cited by 204
Recent Advances in Artificial Intelligence Sensors
Zhang, Z., Wang, L., & Lee, C. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202200072 · Cited by 107
Metadata
- Authors
- Han, J. H., Bae, K. M., Hong, S. K., Park, H., Kwak, J.-H., Wang, H. S., Joe, D. J., Park, J. H., Jung, Y. H., Hur, S., Yoo, C. D., & Lee, K. J.
- Venue
- Nano Energy
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 53
- Pages
- 658-665
- ISSN
- 2211-2855 (ISSN-L)
- OpenAlex
- W2890769863
- Cited by
- 175
- References
- 4
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Han, J. H., Bae, K. M., Hong, S. K., Park, H., Kwak, J.-H., Wang, H. S., Joe, D. J., Park, J. H., Jung, Y. H., Hur, S., Yoo, C. D., & Lee, K. J. (2018). Machine learning-based self-powered acoustic sensor for speaker recognition. Nano Energy. https://doi.org/10.1016/j.nanoen.2018.09.030
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