An improvement of LPC based on noise reduction using pitch synchronous addition

Kuroiwa, Yohei; Shimamura, Tetsuya

doi:10.1109/iscas.1999.778800

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Various studies have been proposed to improve the LP analysis's immunity to noise. A noise reduction method using the technique of pitch synchronous addition was reported in [7]. In this approach, multiple pitch periods taken from the analysis frame are synchronously averaged to obtain the estimated frame.…”

Section: Introductionmentioning

confidence: 99%

A Smoothed and Thresholded Linear Prediction Analysis for Efficient Speech Coding

Alatwi¹,

Paliwal²

2018

jcm

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In this paper, we propose a new method of linear prediction (LP) analysis for estimating LP coefficients that are used in current speech coders. This method improves the robustness of LP coefficients computed from speech signals corrupted by noise as well as offering better quantisation efficiency. The quantisation performance and the noiserobustness obtained by the proposed LP coefficients were compared to that obtained by the LP coefficients computed using the autocorrelation and LP spectrum modification methods of LP Analysis, in terms of spectral distortion (SD). The results indicate that the proposed LP coefficients were more robust to noise and also offered transparent quantisation at lower bit-rates (savings of up to 2 bits/frame) than other LP coefficients.

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Section: Introductionmentioning

confidence: 99%

A Smoothed and Thresholded Linear Prediction Analysis for Efficient Speech Coding

Alatwi¹,

Paliwal²

2018

jcm

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“…The fundamental frequency is a time-varying quantity. Accurate estimation of fundamental frequency of speech signal is useful in speech compression [2], speech enhancement [3], speaker recognition [4], and diagnosis of pathological disorders [5].…”

Section: Introductionmentioning

confidence: 99%

Instantaneous fundamental frequency estimation of speech signals using DESA in low-frequency region

Rathore¹,

Pachori

2013

2013 International Conference on Signal Processing and Communication (Icsc)

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Instantaneous fundamental frequency (IFF) is one of the basic signal parameters of speech signals. Accurate estimation of the IFF of speech signals can be used for speaker recognition system, gender identification and emotion recognition. Various methods for estimation of the IFF of speech signals have been proposed in the literature. In this paper, we propose a new method to estimate IFF of speech signals using the discrete energy separation algorithm (DESA) in the low-frequency region. The Fourier-Bessel (FB) series expansion has been used to filter out the low-frequency region from the speech signal. The reconstructed band-limited signal from the FB coefficients is modeled by using the amplitude and frequency modulated (AM-FM) signal model. Amplitude envelope of the reconstructed band-limited signal is estimated using the DESA. The estimation of amplitude envelope function has been used for detection of glottal closure instants (Gels). Inverse of' time elapsed between two consecutive Gels provides IFF of speech signals. Simulation results of' proposed method have been compared with the IFF computed from reference differenced electroglottograph (EGG) signal. The eMU-Arctic database has been used in this work for validation of the proposed method for IFF determination. It has been observed that the proposed method provides accurate estimation of IFF of speech signals at most of time instants. Key words-Instantaneous fu ndamental fre quency (IFF), Dis crete energy separation algorithm (DESA), Glottal closure instants (Gels), Fo urier-Bessel (FB) series expansion, Amplitude and fr e quency modulated (AM-FM) signal model, Sp eech signal analysis.

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“…A noise reduction approach using the pitch synchronous addition technique was reported in [2]. In that method, the estimated frame is obtained by carrying out a synchronous average of multiple pitch periods included within the analysis frame.…”

Section: Introductionmentioning

confidence: 99%

Improving LPC analysis of speech in additive noise

Trabelsi

Boyer

Savaria

et al. 2007

2007 IEEE Northeast Workshop on Circuits and Systems

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Linear prediction based speech (LPC) analysis is known to be sensitive to the presence of additive noise. In this paper, we present a noise-compensated method for LPC analysis which ensures good spectral matching between the original speech spectrum and the autoregressive (AR) model spectrum. In this method, the noise periodogram is obtained first by applying a simplified noise power spectral density (PSD) estimator on the calculated noisy periodogram. Then, the effect of noise on the spectral parameters is decreased by gradually subtracting values of the resulting noise autocorrelation coefficients from the coefficients derived from the noisy speech. By taking the absolute value of the estimated reflection coefficients as the decision criterion, we show that this iterative procedure ensures a significant decrease of the degrading effect of noise while the estimated autocorrelation matrix is guaranteed to be positive definite. The method was tested on real speech signals and yielded superior performance when compared to conventional LPC analysis, even in severe noisy conditions.

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An improvement of LPC based on noise reduction using pitch synchronous addition

Cited by 10 publications

References 6 publications

A Smoothed and Thresholded Linear Prediction Analysis for Efficient Speech Coding

A Smoothed and Thresholded Linear Prediction Analysis for Efficient Speech Coding

Instantaneous fundamental frequency estimation of speech signals using DESA in low-frequency region

Improving LPC analysis of speech in additive noise

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