Noise-robust pitch detection method using wavelet transform with aliasing compensation

Chen, S. H.; Wang, Jhing-Fa

doi:10.1049/ip-vis:20020242

Cited by 21 publications

(5 citation statements)

References 10 publications

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“…To facilitate the procedure of maxima identification in wavelet coefficients, Chen and Wang (2002) considered using the spatial correlation function that took into account the epochal features in adjacent scales (Xu et al 1994). The spatial correlation function is defined as…”

Section: Enhancement Of Epochal Features Using the Swt-hs Schemementioning

confidence: 99%

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Robust glottal closure instant detection by jointly exploiting stationary wavelet transform and harmonic superposition

Hsu

2015

Int J Speech Technol

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This study jointly utilizes stationary wavelet transform (SWT) and harmonic superposition (HS) techniques to locate zero-crossings closely related to glottal closure instants (GCI). The entire process is performed directly on voiced speech signals without referring to the linear prediction residual or voiced source signal derived by inverse filtering. Subsequent to the multi-scale SWT decomposition, a linear phase FIR filter is introduced to translate positive zero-crossings into pulse-like features. While the product across the approximation coefficients in various SWT levels sharpens impulse features, the HS is employed to sieve out the main pulses corresponding GCIs. The advantages of using the proposed SWT-HS scheme for GCI detection are examined using the PTDB-TUG database. Compared with the other two advanced methods, namely the SEDREAMS and ZFR, without the assistance of any refining process the proposed SWT-HS not only renders better accuracy in GCI positioning but exhibits superior robustness against additive noise.

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Section: Enhancement Of Epochal Features Using the Swt-hs Schemementioning

confidence: 99%

“…The positions with coincident maxima in different scales were recognized as GCIs. Emerging from the above concept, researchers had tried various kinds of WT to perform PD or GCI determination (Bing et al 2005;Chen and Wang 2002;Erçelebi 2003;Messaoud et al 2011;Obaidat et al 1998Obaidat et al , 1999.…”

Section: Introductionmentioning

confidence: 99%

Robust glottal closure instant detection by jointly exploiting stationary wavelet transform and harmonic superposition

Hsu

2015

Int J Speech Technol

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“…For real speech, the filtered signal exhibits some spurious peaks which are to be eliminated by using a suitable peak correction method. Thresholding the strength and the proximity of the adjacent peaks [25] is used in the peak correction algorithm. That is, in the first stage of correction, a peak is validated only if its amplitude is above a threshold.…”

Section: Estimation Of Fmentioning

confidence: 99%

Study of Harmonics-to-Noise Ratio and Critical-Band Energy Spectrum of Speech as Acoustic Indicators of Laryngeal and Voice Pathology

Shama

krishna

Cholayya

2006

EURASIP J. Adv. Signal Process.

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Acoustic analysis of speech signals is a noninvasive technique that has been proved to be an effective tool for the objective support of vocal and voice disease screening. In the present study acoustic analysis of sustained vowels is considered. A simple k-means nearest neighbor classifier is designed to test the efficacy of a harmonics-to-noise ratio (HNR) measure and the critical-band energy spectrum of the voiced speech signal as tools for the detection of laryngeal pathologies. It groups the given voice signal sample into pathologic and normal. The voiced speech signal is decomposed into harmonic and noise components using an iterative signal extrapolation algorithm. The HNRs at four different frequency bands are estimated and used as features. Voiced speech is also filtered with 21 critical-bandpass filters that mimic the human auditory neurons. Normalized energies of these filter outputs are used as another set of features. The results obtained have shown that the HNR and the critical-band energy spectrum can be used to correlate laryngeal pathology and voice alteration, using previously classified voice samples. This method could be an additional acoustic indicator that supplements the clinical diagnostic features for voice evaluation.

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“…Although many methods of pitch estimation for voice signal have been studied for decades, reliable and accurate estimation is still a challenging task, due to the complexity of the speech signal. Basically, there are two kinds of methods, in frequency domain such as [1] method, in time domain such as [2] and [3], most of pitch estimation methods are based on the assumption that speech signal is stationary in short time, but the reality is that speech signal is nonstationary and quasi-periodical, it will sometimes induce estimation error.…”

Section: Introductionmentioning

confidence: 99%

Pitch period estimation of voice signal based on EEMD and Hilbert Transform

Zhang

Ding

Jiang

2010

2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)

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An improved Empirical Mode Decomposition (EMD) named Ensemble Empirical Mode Decomposition (EEMD) is introduced to analyse voice signal. A brief review of EEMD and Hilbert Transform (HT) is given, and a flowchart of EMD/EEMD algorithm is proposed. With EEMD, a voice signal can be decomposed into a complete and finite set of adaptive basis functions defined as Intrinsic Mode Functions (IMFs), which admit well-behaved HT. Then the final presentation of the results is an energy-frequensy-time distribution designated as the Hilbert Spectrum (HS), from which the pitch period of the voice signal can be perfectly estimated. The final experimental results show that HS plays a good performance and makes better results in the voice signal pitch period estimation.Index Terms-ensemble empirical mode decomposition, Hilbert transform, voice signal, pitch period estimation I.

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Noise-robust pitch detection method using wavelet transform with aliasing compensation

Cited by 21 publications

References 10 publications

Robust glottal closure instant detection by jointly exploiting stationary wavelet transform and harmonic superposition

Robust glottal closure instant detection by jointly exploiting stationary wavelet transform and harmonic superposition

Study of Harmonics-to-Noise Ratio and Critical-Band Energy Spectrum of Speech as Acoustic Indicators of Laryngeal and Voice Pathology

Pitch period estimation of voice signal based on EEMD and Hilbert Transform

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