Multichannel Speech Dereverberation using Generalized Regression Neural Network

Authors

  • Seema Arote Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology, Pune, India
  • Vijay Mane Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology, Pune, India
  • Shakil S. Shaikh Pravara Rural Engineering College Loni

Keywords:

Reverberation, Dereverberation, Room Impulse Response (RIR), General Regression Neural Network (GRNN), Signal to noise ratio (SNR)

Abstract

When the sound signal is recorded in a confined room, it gets corrupted by echo and background noise present in room. It also deteriorates the property of the dialogue signal and poses a question for numerous speech-related systems, which includes automatic speech recognition and speaker recognition. The Generalized Regression Neural Network (GRNN), which is a single-pass learning process, is renowned for its capability to quickly train on sparse data sets. In this paper, a GRNN-based approach is implemented, which deals with the unified effects of noisy and reverberant environment.  The presented approach encompasses two phases: a preprocessing phase which contains framing and feature extraction and a dereverberation and denoising phase which uses the common regression neural network. The outcome of the suggested approach is verified in noisy circumstances for variations in noise, reverberation time and signal to noise ratios. The result of the experiment shows that the developed method operates superior than the existing technique for the actual quality measures. STOI is increased by 5.93% and PESQ is increased by 64.73%.

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Published

21.09.2023

How to Cite

Arote, S. ., Mane, V. ., & S. Shaikh, S. . (2023). Multichannel Speech Dereverberation using Generalized Regression Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 46–53. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3453

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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