Wavelet Processing for Pitch Period Estimation

Bernadin, Shonda; Foo, Simon Y.

doi:10.1109/ssst.2006.1619056

Cited by 8 publications

(2 citation statements)

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“…In the field of music and speech analysis, the pitch period (fundamental frequency) estimation is very important. For shorttime estimation of pitch period, the algorithms that find the average fundamental frequency using autocorrelation or linear prediction techniques are the most commonly used (Bernardin, 2006;Guangyu & Shize, 2009;Min, Yingchun, & Zhaohui, 2005;Peeters, 2006).…”

Section: Introductionmentioning

confidence: 99%

Finite-time position and velocity estimation adapted to noisy biased acceleration measurements from periodic motion

Estrada

Efimov

Perruquetti

2016

International Journal of Control

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The present work focuses on the problem of velocity and position estimation. A solution is presented for a class of oscillating systems in which position, velocity and acceleration are zero mean signals. The proposed scheme considers that the dynamic model of the system is unknown. Only noisy acceleration measurements, that may be contaminated by zero mean noise and constant bias, are considered to be available. The proposal uses the periodic nature of the signals obtaining finite-time estimations while tackling integration drift accumulation.

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Section: Introductionmentioning

confidence: 99%

Finite-time position and velocity estimation adapted to noisy biased acceleration measurements from periodic motion

Estrada

Efimov

Perruquetti

2016

International Journal of Control

View full text Add to dashboard Cite

show abstract

“…In the field of music and speech analysis the pitch period (fundamental frequency) estimation is very important. For short-time estimation of pitch period, the algorithms that find the average fundamental frequency using autocorrelation or linear prediction techniques are the most commonly used Guangyu andShize (2009), Peeters (2006), Bernardin (2006), Min et al (2005). We propose a period estimation algorithm that takes advantage on the zero mean nature of the signals.…”

Section: Introductionmentioning

confidence: 99%