Average magnitude difference function pitch extractor

Ross, M.; Shaffer, H.; Cohen, Aaron E.; Freudberg, R.; Manley, Harold J.

doi:10.1109/tassp.1974.1162598

Cited by 426 publications

(157 citation statements)

References 4 publications

Supporting

Mentioning

145

Contrasting

Unclassified

Order By: Relevance

“…Hasan proposed signal reshaping technique [13] for emphasizing the true peak. Shimamura proposed weighted the ACF [14] by the inverse average magnitude difference function [9].…”

Section: Problem Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Correlation Based Fundamental Frequency Extraction Method in Noisy Speech Signal

Hasan¹

2017

IJCSEIT

View full text Add to dashboard Cite

show abstract

“…Hasan proposed signal reshaping technique [13] for emphasizing the true peak. Shimamura proposed weighted the ACF [14] by the inverse average magnitude difference function [9].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…On the other hand, a very popular method, average magnitude difference function (AMDF) [9] has the benefit of low computation and high precision and the calculation cost needed less than that of ACF. The major disadvantages of ACF and AMDF are half pitch error and double pitch error when high noise is introduce with original signal.…”

Section: Introductionmentioning

confidence: 99%

Correlation Based Fundamental Frequency Extraction Method in Noisy Speech Signal

Hasan¹

2017

IJCSEIT

View full text Add to dashboard Cite

show abstract

“…Typical methods are based on the autocorrelation function (ACF) and the average magnitude difference function (AMDF) [4] [5]. They are calculated for each frame with fixed length.…”

Section: Pitch Extractionmentioning

confidence: 99%

“…In this paper, we review the autocorrelation function (ACF) method [4], the average magnitude difference function (AMDF) method [5], and YIN method proposed by Cheveigne and Kawahara [6]. Then a new method based on the normalized second standard deviation function (NSSDF) of magnitude difference is proposed.…”

Section: Introductionmentioning

confidence: 99%

A Pitch Synchronous Method for Speech Modification

Kuo

Wang

2008

2008 6th International Symposium on Chinese Spoken Language Processing

View full text Add to dashboard Cite

Abstract--The speech modification is a mechanism of changing speech characteristics and prosody for some specific applications. It is used in voice conversion, pronunciation correction, tone perception, and language learning. The most important part is the change of pitch in an utterance. Pitch extraction is an essential process for speech modification. This paper presents an efficient pitch extraction algorithm based on the normalized second standard deviation function (NSSDF) of magnitude difference. A pitch synchronous method for modifying speaking rate and pitch trajectory is proposed. The speaking rate is modified by inserting or deleting pitch periods in voiced segments. The pitch trajectory change is accomplished by modifying the pitch period of residual signal obtained from pitch synchronous linear prediction (LP) analysis and reconstructing speech signal by LP filter. A speech modification system is developed for Mandarin perception which is used to help hearing impaired students in pronunciation learning.

show abstract

“…In TDOA estimation, there are two typical algorithms, generalized cross correlation (GCC) [3] and the average magnitude difference function (AMDF) [4]. GCC uses multiplication, and AMDF uses subtraction [5].…”

Section: Introductionmentioning

confidence: 99%

Real-time Sound Localization Using Generalized Cross Correlation Based on 0.13 ㎛ CMOS Process

Jin¹,

Jin²,

Lee³

et al. 2014

JSTS:Journal of Semiconductor Technology and Science

View full text Add to dashboard Cite

Abstract-In this paper, we present the design and implementation of real-time sound localization based on 0.13 µm CMOS process. Time delay of arrival (TDOA) estimation was used to obtain the direction of the sound signal. The sound localization chip consists of four modules: data buffering, short-term energy calculation, cross correlation, and azimuth calculation. Our chip achieved real-time processing speed with full range (360°) using three microphones. Additionally, we developed a dedicated sound localization circuit (DSLC) system for measuring the accuracy of the sound localization chip. The DSLC system revealed that our chip gave reasonably accurate results in an experiment that was carried out in a noisy and reverberant environment. In addition, the performance of our chip was compared with those of other chip designs.

show abstract

Average magnitude difference function pitch extractor

Cited by 426 publications

References 4 publications

Correlation Based Fundamental Frequency Extraction Method in Noisy Speech Signal

Correlation Based Fundamental Frequency Extraction Method in Noisy Speech Signal

A Pitch Synchronous Method for Speech Modification

Real-time Sound Localization Using Generalized Cross Correlation Based on 0.13 ㎛ CMOS Process

Contact Info

Product

Resources

About