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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
Volume 6, Issue 2, April 2017, Pages: 11-17
Received: Sep. 11, 2017; Accepted: Sep. 21, 2017; Published: Oct. 23, 2017
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Authors
Kazi Mahmudul Hassan, Department of Computer Science & Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh
Ekramul Hamid, Department of Computer Science & Engineering, University of Rajshahi, Rajshahi, Bangladesh
Khademul Islam Molla, Department of Computer Science & Engineering, University of Rajshahi, Rajshahi, Bangladesh
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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.
Keywords
Voiced/Unvoiced Classification, Spectrogram Image, Short-time Energy Ratio, Energy Ratio Pattern, Short-time Zero-crossing Rate, White Gaussian Noise
To cite this article
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, Science Journal of Circuits, Systems and Signal Processing. Vol. 6, No. 2, 2017, pp. 11-17. doi: 10.11648/j.cssp.20170602.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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