Open Access

ARTICLE

An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

Girika Jyoshna^1,*, Md. Zia Ur Rahman¹, L. Koteswararao²

1 Deptartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, KL University, Vaddeswaram, Guntur, Andhra Pradesh, India
2 Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, KL University, Hyderabad, Telangana, India

* Corresponding Author: Girika Jyoshna. Email:

Computers, Materials & Continua 2022, 70(2), 3067-3080. https://doi.org/10.32604/cmc.2022.020160

Received 12 May 2021; Accepted 21 June 2021; Issue published 27 September 2021

Abstract

In issues like hearing impairment, speech therapy and hearing aids play a major role in reducing the impairment. Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy. During the transmission of speech signals, several noise components contaminate the actual speech components. This paper addresses a new adaptive speech enhancement (ASE) method based on a modified version of singular spectrum analysis (MSSA). The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component. The MSSA adopts three key steps for generating the reference from the contaminated speech only. These are decomposition, grouping and reconstruction. The generated reference is taken as a reference for variable size adaptive learning algorithms. In this work two categories of adaptive learning algorithms are used. They are step variable adaptive learning (SVAL) algorithm and time variable step size adaptive learning (TVAL). Further, sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning algorithms. The performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement (SNRI), excess mean square error (EMSE) and misadjustment (MSD). For cockpit noise these measures are found to be 29.2850, –27.6060 and 0.0758 dB respectively during the experiments using SVAL algorithm. By considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.

---

Keywords

---