UMP Institutional Repository

Objective and Subjective Evaluations of Adaptive Noise Cancellation Systems with Selectable Algorithms for Speech Intelligibility

Roshahliza, M. Ramli and Salina, Abdul Samad and Noor, Ali O. Abid (2018) Objective and Subjective Evaluations of Adaptive Noise Cancellation Systems with Selectable Algorithms for Speech Intelligibility. Bulletin of Electrical Engineering and Informatics, 7 (4). pp. 570-579. ISSN 2302-9285

[img]
Preview
Pdf
BEEI_Dec2018.pdf
Available under License Creative Commons Attribution.

Download (953kB) | Preview

Abstract

Adaptive Noise Cancellation (ANC) systems with selectable algorithms refer to ANC systems that are able to change the adaptation algorithm based on the eigenvalue spread of the noise. These systems can have dual inputs based on the conventional ANC structure or a single input based on the Adaptive Line Enhancer (ALE) structure. This paper presents a comparison of the performance of these two systems using objective and subjective measurements for speech intelligibility. The parameters used to objectively compare the systems are the Mean Square Error (MSE) and the output Signal to Noise Ratio (SNR). For subjective evaluation, listening tests were evaluated using the Mean Opinion Score (MOS) technique. The outcomes demonstrate that for both objective and subjection evaluations, the single input ALE with selectable algorithms (S-ALE) system outperforms that of the dual input ANC with selectable algorithm (S-ANC) in terms of better steady-state MSE by 10%, higher SNR values for most types of noise, higher scores in most of the questions in the MOS questionnaire and a higher acceptance rate for speech quality.

Item Type: Article
Additional Information: Indexed in Scopus
Uncontrolled Keywords: Adaptive Line Enhancer, Adaptive Noise Cancellation, Selectable algorithms, Speech intelligibility
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Engineering Technology
Depositing User: Dr. Roshahliza M Ramli
Date Deposited: 29 Jan 2019 03:33
Last Modified: 29 Jan 2019 03:33
URI: http://umpir.ump.edu.my/id/eprint/23780
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item