A New Residual Convolutional Neural Network-Based Speech Improvement

Authors

  • M Balasubrahmanyam Assistant Professor, Department of Electronics and Communication Engineering, PACE Institute of Technology and Sciences, Ongole, India Author
  • B Haribabu Assistant Professor, Department of Electronics and Communication Engineering, PACE Institute of Technology and Sciences, Ongole, India Author
  • P Uday Kumar Assistant Professor, Department of Electronics and Communication Engineering, PACE Institute of Technology and Sciences, Ongole, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Convolutional Neural Networks, Liver, Segmentation, Tumor, ResNet, Deep Learning

Abstract

Among the most crucial methods for  denoising a noisy voice signal and enhancing its quality is  speech enhancement. This study makes use of Adaptive  Residual Neural Network technique to reduces maximum off  background noise. This method continuously monitors the  background noise depends upon the environmental changes  using SNR parameter. It has two functions first one is non  linear functions followed by convolutional neural networks  and second one is linearity followed due to Residual neural  networks. By using these factors remove background noise  even SNR is low conditions. Compared to other techniques this  technique is new,fastest, requires less training and also reduces  size. 

Downloads

Download data is not yet available.

References

H. Levitt, "Noise reduct ion in hearing aids: A review," Journal of rehabilitation research and development.

D. Wang, "Deep learning reinvents the hearing aid," IEEE Spectrum.

J. Li, M. Akagi, S. Sakamoto, S. Hongo, & Y. Suzuki, "Two stage binaural speech enhancement with Wiener filter for high quality speech communication," Speech Communication, 2011.

Y. Hu, F. Chen, & M. Yuan, "Evaluat ion of noise reduct ion methods for sentence recognit ion by mandarinspeaking cochlear implant listeners," Ear & hearing, 2015.

Y.-H. Lai, F. Chen, X. Lu, Y. Tsao, S.-S. Wang , & C.-H. Lee, "A Deep Denoising Autoencoder approach to improving the

intelligibility of vocoded speech in cochlear implant simulation," IEEE Trans on Biomedical Engineering, 2016.

L. Deng, J. Li, Y. Gong, and R. Haeb- Umbach, "An overview of noise robust automat ic speech recognition," IEEE/ACM Trans on Audio, Speech, and Language Processing, 2014.

P. Scalart , "Speech enhancement based on a priori signal to noise estimation," in International Conf. on Acoustics, Speech and Signal Processing (ICASSP), 1996.

S. F. Boll, "Suppression of acoustic noise in speech using spectral subtract ion," IEEE Trans on Acoustics, Speech and Signal Processing, 1979.

K. W. Wilson, P. Smaragdis, B. Raj & A. Divakaran, "Speech denoising using nonnegat ive matrix factorizat ion with priors," in Internat ional Conf. on Acoust ics, Speech and Signal Processing (ICASSP), 2008

Szu-Wei Fu, Yu Tsao, Xugang Lu & Hisashi Kawai, “Raw Waveformbased Speech Enhancement by Fully Convolut ional Networks”, P roc. of APSIPA Annual Summit and Conference 2017.

C. Valent ini-Bot inhao, X. Wang, S. akaki and J. Yamagishi, "Speech Enhancement for a Noise-Robust Text -to-Speech Synthesis System using Deep Recurrent Neural Networks", In Proceedings, Interspeech 2016.

Wang, DeLiang, and Jitong Chen. supervised speech separat ion based on deep learning: An overview." IEEE/ACM Trans. on Audio, Speech, and Language Processing (2018).

S. Patibandla, M. Archana, and R. C. Tanguturi, “Object Tracking using Multi Adaptive Feature Extraction Technique,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 279–286, Jun. 2022, doi: 10.14445/22315381/ijett-v70i6p229.

G. Sadineni, A. M, and R. C. Tanguturi, “Optimized Detector Generation Procedure for Wireless Sensor Networks based Intrusion Detection System,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 63–72, Jun. 2022, doi: 10.14445/22315381/ijett-v70i6p208.

S. Patibandla, Dr. M. Archana, and Dr. R. C. Tanguturi, “DATA AGGREGATION BASED HYBRID DEEP LEARNING TECHNIQUE FOR IDENTIFYING THE UNCERTAINTIES AND ACCURATE OBJECT DETECTION,” Indian Journal of Computer Science and Engineering, vol. 13, no. 3, pp. 697–708, Jun. 2022, doi: 10.21817/indjcse/2022/v13i3/221303049.

Dr. S. R. Anand, Dr. R. C. Tanguturi, and S. D S, “Blockchain Based Packet Delivery Mechanism for WSN,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2, pp. 1112–1117, Jul. 2019, doi: 10.35940/ijrte.b1627.078219.

M. V. Bharathi, R. C. Tanguturi, C. Jayakumar, and K. Selvamani, “Node capture attack in Wireless Sensor Network: A survey,” 2012 IEEE International Conference on Computational Intelligence and Computing Research, Dec. 2012, doi: 10.1109/iccic.2012.6510237.

Downloads

Published

2022-11-30

How to Cite

A New Residual Convolutional Neural Network-Based Speech Improvement . (2022). International Journal of Innovative Research in Computer Science & Technology, 10(6), 39–42. https://doi.org/10.55524/