A New Noise Reduction System Based on ALE and Noise Reconstruction Filter

Sasaoka, Naoto; Sumi, Keisuke; Itoh, Yoshio; Fujii, Kensaku

doi:10.1109/iscas.2005.1464577

Cited by 19 publications

(8 citation statements)

References 2 publications

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“…Assuming that the input signal delayed by a pitch period of speech is included in tap inputs, the ALE can estimate only a speech signal s(n) [7]. The distribution of pitch frequency is known to be logarithmical Gaussian type.…”

Section: A Principle Of Variable Step Sizementioning

confidence: 99%

“…About female pitch frequency, the average and standard deviation are 250Hz and 41.0Hz respectively [8]. Therefore, it is calculated that a pitch period ranges from 20 samples to 102 samples when the signal is sampled by 8kHz and occurring probability is 99.7% [7]. Hence, the tap inputs of the ALE are set for x(n−20), x(n−21),..., x(n−102).…”

Section: A Principle Of Variable Step Sizementioning

confidence: 99%

See 1 more Smart Citation

Noise Reduction System Based on LPEF and System Identification with Variable Step Size

Sasaoka

Watanabe

Itoh

et al. 2007

2007 IEEE International Symposium on Circuits and Systems (ISCAS)

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We have investigated a noise reduction method based on a linear prediction error filter (LPEF) and system identification. Background noise is estimated by system identification. In the case of using a fixed step size with which tap coefficients of an adaptive filter is updated, it is difficult to reduce background noise while maintaining the high quality of enhanced speech. Therefore, a variable step size is proposed. In a speech section, a small step size is used so as not to estimate speech. On the other hand, a large step size is used to track background noise in a non-speech section.

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Section: A Principle Of Variable Step Sizementioning

confidence: 99%

Section: A Principle Of Variable Step Sizementioning

confidence: 99%

Noise Reduction System Based on LPEF and System Identification with Variable Step Size

Sasaoka

Watanabe

Itoh

et al. 2007

2007 IEEE International Symposium on Circuits and Systems (ISCAS)

View full text Add to dashboard Cite

show abstract

“…Assuming that the input signal delayed by a pitch period of speech is included in tap inputs, the ALE can estimate only a speech signal s(n) [6]. The distribution of pitch frequency is known to be logarithmical Gaussian type.…”

Section: Estimation Of Cross-correlationmentioning

confidence: 99%

“…About female pitch frequency, the average and standard deviation are 250Hz and 41.0Hz respectively [7]. Therefore, it is calculated that a pitch period ranges from 20 samples to 102 samples in the case of the signal sampled by 8kHz when occurring probability is 99.7% [6]. where γ sξ represents a forgetting factor.…”

Section: Estimation Of Cross-correlationmentioning

confidence: 99%

A Study on Noise Reduction Method Based on LPEF and System Identification with Step Size Control

Sasaoka

Watanabe

Itoh

et al. 2006

2006 IEEE 12th Digital Signal Processing Workshop &Amp;amp; 4th IEEE Signal Processing Education Workshop

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We have proposed a noise reduction method based on a noise reconstruction system (NRS). The NRS uses a linear prediction error filter (LPEF) and a noise reconstruction filter (NRF) which estimates background noise by system identification. In case a fixed step size for updating tap coefficients of a NRF is used, it is difficult to reduce background noise while maintaining the high quality of enhanced speech. In order to solve the problem, the control of a step size is proposed. It takes advantage of crosscorrelations between input signals and an enhanced speech signal. In a speech section, a small step size is used so that speech cannot be estimated. On the other hand, a large step size is used to track non-stationary noise in a non-speech section.

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“…Naoto Sasaoka etc. applied ALE to reduce the sinusoidal noise in noise speech signal [4]. J. R. Mohammed etc.…”

Section: Introductionmentioning

confidence: 99%

A Transient Enhancement Method for Two-Stage Helicopter Gearbox Fault Diagnosis Based on ALE

Tian¹,

Wang²,

Chen³

et al. 2013

JSIP

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Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However, this component is very small in the early stage of the fault and masked by various noises such as gear meshing components modulated by shaft frequency, which make it difficult to extract accurately for fault detection. The adaptive line enhancer (ALE) is an effective technique for separating sinusoidals from broad-band components of an input signal for detecting the presence of sinusoids in white noise. In this paper, ALE is explored to suppress the periodical gear meshing frequencies and enhance the fault feature impulses for more accurate fault diagnosis. The results obtained from simulated and experimental vibration signals of a two stage helical gearbox prove that the ALE method is very effective in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency analysis. The results show a clear difference between the baseline and 30% tooth damage of a helical gear which has not been detected successfully in author's previous studies.

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A New Noise Reduction System Based on ALE and Noise Reconstruction Filter

Cited by 19 publications

References 2 publications

Noise Reduction System Based on LPEF and System Identification with Variable Step Size

Noise Reduction System Based on LPEF and System Identification with Variable Step Size

A Study on Noise Reduction Method Based on LPEF and System Identification with Step Size Control

A Transient Enhancement Method for Two-Stage Helicopter Gearbox Fault Diagnosis Based on ALE

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