• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.88-92
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• 1993

In speech signal processing, it is necessary to detect exactly the pitch. The algorithms of pitch extraction which have been proposed until now are difficult to detect pitches over wide range speech signals. Thus we propose a new algorithm which uses the G-peak extraction to do it. It is the method that finds the most MZI(maximum zero-crossing interval) at each frame and convolve it with speech signal ; this is the same with passing speech signals to variable LPF. Finally we obtained the pitch, improve the accuracy of pitch detection and extract it with the high speed.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.963-970
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• 1994

In speech signal processing, it is very important to detect the pitch exactly. The algorithms for pitch extraction that have been proposed until now are not enough to detect the fine pitch in speech signal. Thus we propose the new algorithm which takes advantage of the G-peak extraction. It is the method to find MZCI(maximum zer-crossing interval) which is defined as cut-off bandwidth rate of LPF (low pass filter)and detect the pitch period of the voiced signals. This algorithm performs robustly with a gross error rate of 3.63% even in 0 dB SNR environment. The gross error rate for clean speech is only 0.18%. Also it is able to process all course with speed.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1213-1228
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• 2017

The pitch tracking of music has been researched for several decades. Several possible improvements are available for creating a good t-distribution, using the instantaneous robust algorithm for pitch tracking framework to perfectly detect pitch. This article shows how to detect the pitch of music utilizing an improved detection method which applies a statistical method; this approach uses a pitch track, or a sequence of frequency bin numbers. This sequence is used to create an index that offers useful features for comparing similar songs. The pitch frequency spectrum is extracted using a modified instantaneous robust algorithm for pitch tracking (IRAPT) as a base combined with the statistical method. The pitch detection algorithm was implemented, and the percentage of performance matching in Thai classical music was assessed in order to test the accuracy of the algorithm. We used the longest common subsequence to compare the similarities in pitch sequence alignments in the music. The experimental results of this research show that the accuracy of retrieval of Thai classical music using the t-distribution of instantaneous robust algorithm for pitch tracking (t-IRAPT) is 99.01%, and is in the top five ranking, with the shortest query sample being five seconds long.

• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.221-224
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• 1995

In speech signal processing, it is necessary to detect exactly the pitch. The algorithms of pitch extraction with have been proposed until now are difficult to detect pitches over wide range speech signals. In this paper, thus, we proposed a new pitch detection algorithm that used a low pass filter with variable bandwidth. It is the method that preprosses to find the first formant of speech signals by the FFT at each frame and detects the pitches for signals LPFed with the cut off frequency according to the first formant. Applying the method, we obtained the pitch contours, improving the accuracy of pitch detection in some noise environments.

• Speech Sciences
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• v.10 no.2
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• pp.85-95
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• 2003

In speech signal processing, it is very important to detect the pitch exactly in speech recognition, synthesis and analysis. If we exactly pitch detect in speech signal, in the analysis, we can use the pitch to obtain properly the vocal tract parameter. It can be used to easily change or to maintain the naturalness and intelligibility of quality in speech synthesis and to eliminate the personality for speaker-independence in speech recognition. In this paper, we proposed a new pitch detection algorithm. First, positive center clipping is process by using the incline of speech in order to emphasize pitch period with a glottal component of removed vocal tract characteristic in time domain. And rough formant envelope is computed through peak-fitting spectrum of original speech signal infrequence domain. Using the roughed formant envelope, obtain the smoothed formant envelope through calculate the linear interpolation. As well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. Inverse fast fourier transform (IFFT) compute this flattened harmonics. After all, we obtain Residual signal which is removed vocal tract element. The performance was compared with LPC and Cepstrum, ACF. Owing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• The Journal of the Acoustical Society of Korea
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• v.13 no.5
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• pp.77-82
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• 1994

In speech signal processing, it is very important to detect the pitch exactly. Although various methods for detecting the pitch of speech signals have been developed, it is difficult to exactly extract the pitch for wide range of speakers and various utterances. Thus we propose a new pitch detection algorithm which takes advantage of the G-peak extraction. It is a method to detect the pitch period of the voiced signals by finding MZCI (maximum zero-crossing interval) of the G-peak which is defined as cut-off bandwidth rate of LPF (low pass filter). This algorithm performs robustly with a gross error rate of 3.63% even in 0 dB SNR environement. The gross error rate for clean speech is only 0.18%. Also it is able to process all courses with high speed.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.381-384
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• 2002

In speech signal processing, it Is very important to detect the pitch exactly in speech recognition, synthesis and analysis. but, it is very difficult to pitch detection from speech signal because of formant and transition amplitude affect. therefore, in this paper, we proposed a pitch detection using the spectrum flattening techniques. Spectrum flattening is to eliminate the formant and transition amplitude affect. In time domain, positive center clipping is process in order to emphasize pitch period with a glottal component of removed vocal tract characteristic. And rough formant envelope is computed through peak-fitting spectrum of original speech signal in frequency domain. As a results, well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. After all, we obtain residual signal which is removed vocal tract element The performance was compared with LPC and Cepstrum, ACF 0wing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1610-1614
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• 1987

The harmonies of the fundamental frequency in speech signal make a minute line spectrum in frequency domain. In this paper, we propose a new algorithm to detect a pitch interval in voiced sound based on the fact that the number of harmonies can represent the period of the pitch in the time domain.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.197-200
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• 2000

This paper presents a time-based Pitch Determination Algorithm (PDA) for reliable estimation of pitch Period (PP) in speech signal. In proposed method, we use the Dyadic Wavelet Transform (DyWT), which detects the presence of Glottal Closure Instants (GCI) and uses the information to determine the pitch period. And, the proposed method also uses the periodicity property of DyWT to detect unsteady GCI. To evaluate the performance of the proposed methods, that of other PDAs based on DyWT are compared with what this paper proposed. The effectiveness of the proposed method is tested with real speech signals containing a transition between voiced and the unvoiced interval where the energy of voiced signal is unsteady. The result shows that the proposed method provides a good performance in estimating the both the unsteady GCI positions as well as the steady parts.

• 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.