• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.235-242
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• 2017

Peaks (or Nulls) finding algorithms for Average Magnitude Difference Function (AMDF) of speech signal are proposed in this paper. Both AMDF and Autocorrelation Function (ACF) are widely used to estimate a pitch of speech signal. It is well known that the estimation of the fundamental requency (F0) for speech signal is not only important but also very difficult. In this paper, two algorithms, are exploited the characteristics of AMDF, are proposed. First, the proposed algorithm which has a Threshold value is applied to the local minima to detect a pitch period. The Other proposed algorithm to estimate a pitch period of speech signal is utilized the relationship between AMDF and ACF. The data in this paper, is recorded by using general commercial device, is composed of Korean emotion expression words. The recorded speech data are applied to two proposed algorithms and tested their performance.

• ETRI Journal
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• v.38 no.3
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• pp.425-434
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• 2016

A novel average magnitude difference function (AMDF)-based pitch detection scheme (PDS) is proposed to achieve better performance in speech quality. A performance evaluation of the proposed PDS is carried out through both a simulation and a real-time implementation of a speech analysis-synthesis system. The parameters used to compare the performance of the proposed PDS with that of PDSs that are based on either a cepstrum, an autocorrelation function (ACF), an AMDF, or circular AMDF (CAMDF) methods are as follows: percentage gross pitch error (%GPE); a subjective listening test; an objective speech quality assessment; a speech intelligibility test; a synthesized speech waveform; computation time; and memory consumption. The proposed PDS results in lower %GPE and better synthesized speech quality and intelligibility for different speech signals as compared to the cepstrum-, ACF-, AMDF-, and CAMDF-based PDSs. The computational time of the proposed PDS is also less than that for the cepstrum-, ACF-, and CAMDF-based PDSs. Moreover, the total memory consumed by the proposed PDS is less than that for the ACF- and cepstrum-based PDSs.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.4
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• pp.38-44
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• 1980

We present a high speed pitch estimation algorithm that is based on peak detection and average magnitude difference function (AMDF). A few pitch candidates are first estimated from the low-pass filtered (800 Hz) speech by a peak detection algorithm. AMDF values of the pitch candidatestare then calculated, and the pitch candidate that yields the minimum AMDF value is chosen as the desired pitch period. The new method requires far less computation time than other pitch estimation algorithms, while it yields fairly accurate results.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.249-255
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• 2016

Since the elderly voices include a lot of noise caused by physiological changes in respiration, phonation, and resonance, the performance of the convergence health-care equipments such as speech recognition, synthesis, analysis program done by elderly voice is deteriorated. Therefore it is necessary to develop researches to operate health-care instruments with elderly voices. In this study, a voice activity detection using a symmetric higher-order differential energy function (SHODEO) was developed and was compared with auto-correlation function(ACF) and the average magnitude difference function(AMDF). It was confirmed to have a better performance than other methods in the voice interval detection. The voice activity detection will be applied to a voice interface for the elderly to improve the accessibility of the smart devices.

• Proceedings of the KAIS Fall Conference
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• 2002.11a
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• pp.212-215
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• 2002

유성음에서의 피치 측정 알고리즘은 보편적이고 단순하여 구현하기 용이하다. 피치는 간단한 전환을 통해 음정을 구할 수 있다. 이렇게 구한 음정 정보를 이용하여 미디를 이용한 자동반주기의 음정 조절을 가능하게 할 수 있다. 본 연구에서는 쉽게 피치를 구하기 위해 저가의 방식인 AMDF(Average Magnitude Difference Function) 알고리즘을 이용하여 피치를 구하였고 이를 미디 음정 정보로 전환하기 위한 방법을 제안하였다. 이를 이용하면 가수의 음정에 맞게 자동반주기가 음정을 보정하여 음악을 연주하여 줄 수 있다.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.75-82
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• 1990

The purpose of this study is to help the hearing Impaired's speech training with a visible speech processing system. In brief, this system converts the features of speech signals into graphics on monitor, and adjusts the features of hearing impaired to normal ones. There are formant and pitch in the features used for this system. They are extracted using the digital signal processing such as linear predictive method or AMDF(Average Magnitude Difference Function). In order to effectively train for the hearing impaired's abnormal speech, easilly visible feature has been being studied.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06d
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• pp.64-67
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• 1998

원격지간 화상회의 시스템에서 화자의 위치에 따른 카메라 제어를 위해서는 마이크로폰 배렬(Microphone Array)로 수음한 음성신호에 대해 각 마이크로폰간의 빠른 지연시간 추정이 요구된다. 본 연구에서는 음원위치 추정을 위한 지연시간(Time delay) 계산을 위해 AMDF(Average Magnitude Difference Function)를 적용하여 연산시간을 단축시키는데 목적을 두고 있다. 기본의 상호상관함수 (Cross-correlation )알고리즘 과 본 연구에서 적용한 AMDF 알고리즘을 비교하기 위해 SNR 10dB 와 20dB 인 200Hz, 500Hz, 1kHz, 2kHz의 정현파 합성신호와 단음절 음성신호에 대해 시뮬레이션을 행하였다. 시뮬레이션 결과 AMDF 알고리즘의 정확한 지연시간 추정을 확인하였다.

• 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.

• 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.