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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 10(1) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
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pp. 3209-3220
S&M2333 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2860 Published: October 9, 2020 Voice Recognition and Marking Using Mel-frequency Cepstral Coefficients [PDF] Jia-Shing Sheu and Ching-Wen Chen (Received March 14, 2020; Accepted June 17, 2020) Keywords: mel-frequency, Hamming window, speech recognition, fast Fourier transform, microphone array
A real-time voice recognition and marking system was developed in this study to automatically identify different voices of speakers. A microphone array was installed for audio reception. Pre-emphasis, framing and Hamming window, fast Fourier transform, mel-frequency, and mel-frequency cepstral coefficients with processing times of 0.001, 0.305, 0.205, 0.049, and 0.546 s, respectively, were used in the system. The total processing time was less than 1.5 s. Unique eigenvalues were obtained for each sound. The results indicated that the proposed system, which is an example of intelligent recording, can be used to automatically record speech in meetings or during classes.
Corresponding author: Jia-Shing Sheu  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Jia-Shing Sheu and Ching-Wen Chen, Voice Recognition and Marking Using Mel-frequency Cepstral Coefficients, Sens. Mater., Vol. 32, No. 10, 2020, p. 3209-3220. |
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