Next Article in Journal
Impedance-Based Non-Destructive Testing Method Combined with Unmanned Aerial Vehicle for Structural Health Monitoring of Civil Infrastructures
Next Article in Special Issue
Effects of the Concentration of Eu3+ Ions and Synthesizing Temperature on the Luminescence Properties of Sr2−xEuxZnMoO6 Phosphors
Previous Article in Journal
An Equivalent Layer-Wise Approach for the Free Vibration Analysis of Thick and Thin Laminated and Sandwich Shells
Previous Article in Special Issue
Enhancement and Reduction of Nonradiative Decay Process in Organic Light-Emitting Diodes by Gold Nanoparticles
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(1), 9; doi:10.3390/app7010009

Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties

1
Department of Information Communication, Asia University, Taichung City 41354, Taiwan
2
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City 40447, Taiwan
3
Department of Multimedia and Game Science, Asia-Pacific Institute of Creativity, Miaoli County 35153, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Shoou-Jinn Chang and Stephen D. Prior
Received: 16 October 2016 / Revised: 6 December 2016 / Accepted: 15 December 2016 / Published: 22 December 2016
View Full-Text   |   Download PDF [1625 KB, uploaded 23 December 2016]   |  

Abstract

The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speech distortion for speech denoising. Conversely, a great quantity of residual noise will occur when the noise magnitude is underestimated. Accurately estimating noise magnitude is important for speech denoising. This study proposes employing variable segment length for noise tracking and variable thresholds for the determination of speech presence probability, resulting in the performance improvement for a minima-controlled-recursive-averaging (MCRA) algorithm in noise estimation. Initially, the fundamental frequency was estimated to determine whether a frame is a vowel. In the case of a vowel frame, the increment of segment lengths and the decrement of threshold for speech presence were performed which resulted in underestimating the level of noise magnitude. Accordingly, the speech distortion is reduced in denoised speech. On the contrary, the segment length decreases rapidly in noise-dominant regions. This enables the noise estimate to update quickly and the noise variation to track well, yielding interference noise being removed effectively through the process of speech denoising. Experimental results show that the proposed approach has been effective in improving the performance of the MCRA algorithm by preserving the weak vowels and consonants. The denoising performance is therefore improved. View Full-Text
Keywords: noise estimation; variable segment length; speech denoising; harmonic adaptation; minimum-controlled-recursive-controlled averaging noise estimation; variable segment length; speech denoising; harmonic adaptation; minimum-controlled-recursive-controlled averaging
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lu, C.-T.; Lei, C.-L.; Shen, J.-H.; Wang, L.-L.; Tseng, K.-F. Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties. Appl. Sci. 2017, 7, 9.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top