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Publication
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| Title of Article |
Pitch segmentation of speech signals based on short-time energy waveform |
| Date of Acceptance |
12 September 2017 |
| Journal |
| Title of Journal |
International Journal of Speech Technology |
| Standard |
SCOPUS |
| Institute of Journal |
springer |
| ISBN/ISSN |
ISSN 1381-2416 |
| Volume |
2017 |
| Issue |
20 |
| Month |
September |
| Year of Publication |
2018 |
| Page |
907-917 |
| Abstract |
In general, speech is constituted of quasi-repetitive
patterns called pitches representing the speech fundamental
period and tonal information of the voice. Extraction
of pitch information that is crucial for many speech processing
techniques, usually faces a noise problem and interference
caused by high-order harmonic components. This
paper introduces a novel, noise-robust method for determining
speech fundamental frequency and pitch segmentation,
based on a short-time energy waveform (SEW), defined as
a moving average squared signal. When applying a moving
average filter with a window size closed to the fundamental
period, nearly repetitive patterns, with fewer ripples, synchronizing
with actual pitches can clearly be observed in
the SEW. The DC component in the SEW is removed using
morphological top-hat and bottom-hat transforms. The fundamental
frequency is determined as the frequency corresponding
to the largest peak of the power spectrum of the
DC-removed SEW. Finally, a time-domain window search
is then performed to locate local extrema associated with
pitches. Compared to traditional pitch detection techniques,
the proposed technique yields pitch segmentation results
with a higher rate of accuracy and greater noise robustness. |
| Keyword |
Pitch detection, Pitch segmentation, Fundamental frequency, Speech signal, Voice signal, Short-time energy waveform |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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