• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.178-183
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• 2006

As a lot of researches on the speech signal processing are performed due to the recent rapid development of the information-communication technology. the pitch period is used as an important element to various speech signal application fields such as the speech recognition. speaker identification. speech analysis. or speech synthesis. A variety of algorithms for the time and the frequency domains related with such pitch period detection have been suggested. One of the pitch detection algorithms for the time domain. AMDF (average magnitude difference function) uses distance between two valley points as the calculated pitch period. However, it has a problem that the algorithm becomes complex in selecting the valley points for the pitch period detection. Therefore, in this paper we proposed the modified AMDF(M-AMDF) algorithm which recognizes the entire minimum valley points as the pitch period of the speech signal by using the rotation transform of AMDF. In addition, a threshold is set to the beginning portion of speech so that it can be used as the selection criteria for the pitch period. Moreover the proposed algorithm is compared with the conventional ones by means of the simulation, and presents better properties than others.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.23-28
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• 2006

Pitch period that is a important factor in speech signal processing is used in various applications such as speech recognition, speaker identification, speech analysis and synthesis. So many pitch detection algorithms have been studied until now. AMDF which is one of pitch period detection algorithms chooses the time interval from valley point to valley point as pitch period. In selection of valley point to detect pitch period, complexity of the algorithm is increased. So in this paper we proposed the simple algorithm using rotation transform of AMDF that detects global minimum valley point as pitch period of speech signal and compared it with existing methods through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.515-519
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• 2005

Pitch period that is a important factor in speech signal processing is used in various applications such as speech recognition, speaker identification, speech analysis and synthesis. So many pitch detection algoritms have been studied until now. AMDF which is one of pitch period detection algorithms chooses the time interval from valley point to valley point as pitch period. In selection of valley point to detect pitch period, complexity of the algoritm is increased. So in this paper we proposed the simple algorithm using modified AMDF that detects global minimum valley point as pitch period of speech signal and compared existing methods with it through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.1019-1022
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• 2005

As recent information communication technology is rapidly developed, a lot of researches related to speech signal processing have been processed. So pitch period is applied as important factor to many application fields such as speech recognition, speaker identification, speech analysis and synthesis. Therefore, many algorithms related to pitch detection have been proposed in time domain and frequency domain and AMDF(average magnitude difference function) which is one of pitch detection algorithms in time domain chooses time interval from valley to valley as pitch period. But, in selection of valley point to detect pitch period, complexity of the algorithm is increased. So in this paper we proposed pitch detection algorithm using rotation transform of AMDF, that taking the global minimum valley point as pitch period and established a threshold about the phoneme in beginning portion, to exclude pitch period selection. and compared existing methods with proposed method through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.481-484
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• 2006

Pitch period detection algorithms are applied to various speech signal processing fields such as speech recognition, speaker identification, speech analysis and synthesis. Furthermore, many pitch detection algorithms of time and frequency domain have been studied until now. AMDF(average magnitude difference function) ,which is one of pitch period detection algorithms, chooses a time interval from the valley point to the valley point as the pitch period. AMDF has a fast computation capacity, but in selection of valley point to detect pitch period, complexity of the algorithm is increased. In order to apply pitch period detection algorithms to the real world, they have robust prosperities against generated noise in the subway environment etc. In this paper we proposed the modified AMDF algorithm which detects the global minimum valley point as the pitch period of speech signals and used speech signals of noisy environments as test signals.

• The Journal of the Acoustical Society of Korea
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• v.31 no.7
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• pp.419-426
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• 2012

The pitch searching is a vital process in a vocoder. Generally, the method of pitch searching is employed after highlighting the periodicity, where a correlation is identified with the signal by changing the interval of two pulses. When the correlation value reaches the peak, the pitch can be found by the pulse interval because it is the repetition interval with most striking period. However if the identified period happens to be one of half period, double period or triple period, this cannot be considered as the pitch period. Many methods were suggested to solve this problem. An inaccurate pitch could be obtained as well, when there is an interval where signal amplitude is not constant but varies abruptly in the frame. To solve this matter, searching the pitch by dividing a frame into various subframes is adopted, but too much calculation has to be followed while it leads the correct value. This paper suggests an algorithm to resolve these two problems. First, to search the pitch after advance correction of the signal energy level with an estimated overall energy change ratio in the frame before pitch search to reduce half period, double period and triple period is suggested. Second, to vary the number of subframes by predicting the amplitude change rate in the frame by the energy ratio obtained by the above-mentioned method is advised. If these two methods are applied, the pitch searching time can be reduced and the general pitch searching performance can be improved without affecting the sound quality in the synthesized signal.

• Speech Sciences
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• v.11 no.3
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• pp.157-164
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• 2004

Human has average pitch-level when speak naturally. That is 'Habitual pitch level'. However, if noise added at speech, the pitch-wave is changed irregularly. We can estimate noise level of speech by using this point. This paper calculates energy level of the input speech, pitch period from of above limited energy level by NAMDF (Normalized Average Magnitude Difference Function) method, after cut each frame by pitch period unit, and propose a method that estimate noise level through closed pitch of input speech.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.52 no.4
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• pp.339-346
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• 2016

The aim of this research was to the experimental data using statistical and spectral analyzing method to get the motion reponses of a stern trawler in operation states such as drifting, sailing and trawling according to the wave height. In drifting, the significant and the maximum valuer of roll in beam sea increased according to the wave height, but those of pitch decreased. The response and the period of peak of roll in beam sea were increased, but those of pitch decreased. In navigation, the significant and maximum values of roll increased remarkably according to the wave height, but those of pitch changed a little. The response of roll was highest in quartering sea, beam sea and then following sea, but those of pitch was highest in bow sea, head sea and then beam sea in the order of all wave heights. The period of peak of roll due to the wave height and the wave direction changed from 3.8 to 9.9 seconds, and those of pitch changed from 3.3 to 10.4 seconds. In trawling, the significant and maximum values of roll increased a little according to the wave height, but those of pitch increased significantly. The response of roll was highest in beam sea, bow sea and then quartering sea, but those of pitch was highest in head sea, following sea, and then beam sea in the order. The period of peak of roll due to the wave height and the direction changed from 6.6 to 10.9 seconds, and those of pitch changed from 6.7 to 11.2 seconds.

• Speech Sciences
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• v.7 no.4
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• pp.3-10
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• 2000

This paper presents a time-based Pitch Determination Algorithm (PDA) for the reliable estimation of Pitch Period (PP) in speech signals. Based on the Dyadic Wavelet Transform (DyWT) , the proposed PDA detects the presence of Glottal Closure Instants (GCI) and uses the information to determine the pitch period. We also examine the problem of conventional PDAs based on DyWT; their performance is compared with the proposition of this paper. The effectiveness of the proposed method is tested with real speech signals containing a transition between the voiced and the unvoiced interval where the energy of the voiced signal is unsteady. The result shows that the proposed method provides good performance in estimating both the unsteady GCI positions as well as the steady parts.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.6
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• pp.330-333
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• 2000

In this paper, a new pitch estimation method is proposed using the Robinson cochlear model. This estimation method is useful in noisy environments and especially very efficient under cochannel in which two speaker voices exist at the same time. For the one speaker speech, the pitch can be extracted from just the neurogram of the Robinson cochlear model. In this case, as the estimation is performed in time domain, the exact pitch period can be detected though the pitch period is various. But in noisy and cochannel cases, the neurogram has many spurious peaks, so we use the autocorrelators in the neurogram to manifest the period. It the autocorrelators are used for the all delays, the large amount of calculations is necessary. Due to this defect, we propose that the autocorrelators are used for the part of the delays on which energy is concentrated. First of all, the proposed algorithm is applied to the one speaker speech, and later to the cochannel speech. And then the results are compared with the autocorrelation pitch detection method.