Track and Noise Separation Based on the Universal Codebook and Enhanced Speech Recognition Using Hybrid Deep Learning Method-Reference-Cited by-同舟云学术

Track and Noise Separation Based on the Universal Codebook and Enhanced Speech Recognition Using Hybrid Deep Learning Method

Published:2023 Issue: Volume:11 Page:120707-120720
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kumer S. V. Aswin¹^ORCID,Gogu Lakshmi Bharath¹,Mohan E.²^ORCID,Maloji Suman¹,Natarajan Balaji³^ORCID,Sambasivam G.⁴^ORCID,Tyagi Vaibhav Bhushan⁵^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

2. Department of ECE, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India

3. Department of Computer Science and Engineering, Sri Venkateshwaraa College of Engineering and Technology, Ariyur, Puducherry, India

4. School of Computing and Data Science, Xiamen University Malaysia, Sepang, Selangor, Malaysia

5. Faculty of Engineering, ISBAT University, Kampala, Uganda

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10298228.pdf?arnumber=10298228

Reference40 articles.

1. Speech enhancement using degenerate unmixing estimation technique and adaptive noise cancellation technique as a post signal processing

2. Convolutive Transfer Function-Based Multichannel Nonnegative Matrix Factorization for Overdetermined Blind Source Separation

3. Phase estimation in single-channel speech enhancement using phase invariance constraints

4. A Unified Approach for Simultaneous Graph Learning and Blind Separation of Graph Signal Sources

5. Codebook driven short-term predictor parameter estimation for speech enhancement