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DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network

Author

Listed:
  • Ping-Huan Kuo
  • Ssu-Ting Lin
  • Jun Hu

Abstract

Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.

Suggested Citation

  • Ping-Huan Kuo & Ssu-Ting Lin & Jun Hu, 2020. "DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720923529
    DOI: 10.1177/1550147720923529
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    References listed on IDEAS

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    1. Shengli Zhou & Kuiying Yin & Fei Fei & Ke Zhang, 2019. "Surface electromyography–based hand movement recognition using the Gaussian mixture model, multilayer perceptron, and AdaBoost method," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    2. Zuojin Li & Qing Yang & Shengfu Chen & Wei Zhou & Liukui Chen & Lei Song, 2019. "A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    3. Shan Meng & Xin Dai & Bicheng Xiao & Yimin Zhou & Yumei Li & Chong Gao, 2019. "Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks," International Journal of Distributed Sensor Networks, , vol. 15(8), pages 15501477198, August.
    4. Van Quan Nguyen & Tien Nguyen Anh & Hyung-Jeong Yang, 2019. "Real-time event detection using recurrent neural network in social sensors," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.
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    Cited by:

    1. Chi-Hua Chen & Kuo-Ming Chao & Feng-Jang Hwang & Chunjia Han & Lianrong Pu, 2021. "Editorial," International Journal of Distributed Sensor Networks, , vol. 17(2), pages 15501477219, February.

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