This paper presents a voiced/unvoiced classification algorithm of the noisy speech signal by analyzing two acoustic features of the speech signal. Short-time energy and short-time zero- crossing rates are one of the most distinguishable time domain features of a speech signal to classify its voiced activity into voiced/unvoiced segment. A new idea is developed where frame by frame processing has done in narrow band speech signal using spectrogram image. Two time domain features, short-time energy (STE) and short-time zero-crossing rate (ZCR) are used to classify its voiced/unvoiced parts. In the first stage, each frame of the analyzing spectrogram is divided into three separate sub bands and examines their short-time energy ratio pattern. Then an energy ratio pattern matching look up table is used to classify the voicing activity. However, this method successfully classifies patterns 1 through 4 but fails in the rest of the patterns in the look up table. Therefore, the rest of the patterns are confirmed in the second stage where frame wise short-time average zero- crossing rate is compared with a threshold value. In this study, the threshold value is calculated from the short-time average zero-crossing rate of White Gaussian Noise (wGn). The accuracy of the proposed method is evaluated using both male and female speech waveforms under different signal-to-noise ratios (SNRs). Experimental results show that the proposed method achieves better accuracy than the conventional methods in the literature.
| Published in | Science Journal of Circuits, Systems and Signal Processing (Volume 6, Issue 2) |
| DOI | 10.11648/j.cssp.20170602.12 |
| Page(s) | 11-17 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Voiced/Unvoiced Classification, Spectrogram Image, Short-time Energy Ratio, Energy Ratio Pattern, Short-time Zero-crossing Rate, White Gaussian Noise
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APA Style
Kazi Mahmudul Hassan, Ekramul Hamid, Khademul Islam Molla. (2017). A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image. Science Journal of Circuits, Systems and Signal Processing, 6(2), 11-17. https://doi.org/10.11648/j.cssp.20170602.12
ACS Style
Kazi Mahmudul Hassan; Ekramul Hamid; Khademul Islam Molla. A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image. Sci. J. Circuits Syst. Signal Process. 2017, 6(2), 11-17. doi: 10.11648/j.cssp.20170602.12
AMA Style
Kazi Mahmudul Hassan, Ekramul Hamid, Khademul Islam Molla. A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image. Sci J Circuits Syst Signal Process. 2017;6(2):11-17. doi: 10.11648/j.cssp.20170602.12
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Download@article{10.11648/j.cssp.20170602.12,
author = {Kazi Mahmudul Hassan and Ekramul Hamid and Khademul Islam Molla},
title = {A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {6},
number = {2},
pages = {11-17},
doi = {10.11648/j.cssp.20170602.12},
url = {https://doi.org/10.11648/j.cssp.20170602.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20170602.12},
abstract = {This paper presents a voiced/unvoiced classification algorithm of the noisy speech signal by analyzing two acoustic features of the speech signal. Short-time energy and short-time zero- crossing rates are one of the most distinguishable time domain features of a speech signal to classify its voiced activity into voiced/unvoiced segment. A new idea is developed where frame by frame processing has done in narrow band speech signal using spectrogram image. Two time domain features, short-time energy (STE) and short-time zero-crossing rate (ZCR) are used to classify its voiced/unvoiced parts. In the first stage, each frame of the analyzing spectrogram is divided into three separate sub bands and examines their short-time energy ratio pattern. Then an energy ratio pattern matching look up table is used to classify the voicing activity. However, this method successfully classifies patterns 1 through 4 but fails in the rest of the patterns in the look up table. Therefore, the rest of the patterns are confirmed in the second stage where frame wise short-time average zero- crossing rate is compared with a threshold value. In this study, the threshold value is calculated from the short-time average zero-crossing rate of White Gaussian Noise (wGn). The accuracy of the proposed method is evaluated using both male and female speech waveforms under different signal-to-noise ratios (SNRs). Experimental results show that the proposed method achieves better accuracy than the conventional methods in the literature.},
year = {2017}
}
TY - JOUR T1 - A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image AU - Kazi Mahmudul Hassan AU - Ekramul Hamid AU - Khademul Islam Molla Y1 - 2017/10/23 PY - 2017 N1 - https://doi.org/10.11648/j.cssp.20170602.12 DO - 10.11648/j.cssp.20170602.12 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 11 EP - 17 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.20170602.12 AB - This paper presents a voiced/unvoiced classification algorithm of the noisy speech signal by analyzing two acoustic features of the speech signal. Short-time energy and short-time zero- crossing rates are one of the most distinguishable time domain features of a speech signal to classify its voiced activity into voiced/unvoiced segment. A new idea is developed where frame by frame processing has done in narrow band speech signal using spectrogram image. Two time domain features, short-time energy (STE) and short-time zero-crossing rate (ZCR) are used to classify its voiced/unvoiced parts. In the first stage, each frame of the analyzing spectrogram is divided into three separate sub bands and examines their short-time energy ratio pattern. Then an energy ratio pattern matching look up table is used to classify the voicing activity. However, this method successfully classifies patterns 1 through 4 but fails in the rest of the patterns in the look up table. Therefore, the rest of the patterns are confirmed in the second stage where frame wise short-time average zero- crossing rate is compared with a threshold value. In this study, the threshold value is calculated from the short-time average zero-crossing rate of White Gaussian Noise (wGn). The accuracy of the proposed method is evaluated using both male and female speech waveforms under different signal-to-noise ratios (SNRs). Experimental results show that the proposed method achieves better accuracy than the conventional methods in the literature. VL - 6 IS - 2 ER -
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