Research on Pitch Extraction Algorithm of Noisy Speech

Pitch detection in noisy environment plays an important role in speech analyzing and recognition. In the light of the properties of Hilbert-Huang transform and the EMD soft-threshold de-noising method, an effective pitch detection method for noisy speech signal is proposed in this paper. Firstly, the EMD soft-threshold de-noising method is applied to realize the background noise reduction, secondly, using the Hilbert-Huang transform to detect the pitch period of the de-noising speech signal. The analysis proposed in this paper show that, compared with the conventional methods of the pitch detection of the noisy speech, especially for the low signal to noise ratio (SNR), this approach has a higher accuracy.

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Pitch Detection Method Based on HHT

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1111 ◽

2013 ◽

Vol 846-847 ◽

pp. 1111-1114

Author(s):

Yao Qi Wang ◽

Xiao Peng Wang ◽

Tao Lei

Keyword(s):

Speech Signal ◽

Detection Method ◽

New Method ◽

Pitch Detection ◽

Noisy Speech ◽

Hilbert Huang Transform ◽

Morphological Filtering ◽

Instantaneous Energy

A new method of pitch detection is proposed based on Hilbert-Huang Transform (HHT). Firstly noisy speech signal is filtered by morphological filtering to remove the noise, and then HHT is employed to get Hilbert-Huang spectrum and to calculate instantaneous energy and its derivative. Distinguish the unvoiced and voiced using mutation of instantaneous energy and track pitch.

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Pitch Detection Method Based on Morphological Filtering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.433 ◽

2014 ◽

Vol 596 ◽

pp. 433-436 ◽

Cited By ~ 1

Author(s):

Yao Qi Wang ◽

Xiao Peng Wang ◽

Lv Cheng Wang

Keyword(s):

Speech Signal ◽

Detection Method ◽

Pitch Detection ◽

Pitch Period ◽

Adaptive Process ◽

Low Snr ◽

Morphological Filtering ◽

The Moment ◽

Opening And Closing ◽

Instantaneous Energy

A new method of pitch detection based on morphological filtering is proposed. Noisy speech signal is filtered by morphological filtering to remove the noise and highlight pitch, and then HHT is employed to get Hilbert-Huang spectrum and to calculate instantaneous energy and its derivative. The moment of glottal opening and closing can be accurately located through tracking mutation of instantaneous energy, so that variation of pitch period can be accurately tracked. Compared with other traditional method of pitch detection, this method not only truly describes non-stationary and non-linear characteristics of speech signal, but also it is an adaptive process for the analysis of the speech signal. The experiments showed that the method has strong anti-noise and can accurately detect the pitch of speech in low SNR.

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Negative impacts from latency masked by noise in simulated beamforming

PLoS ONE ◽

10.1371/journal.pone.0254119 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254119

Author(s):

Jordan A. Drew ◽

W. Owen Brimijoin

Keyword(s):

Speech Signal ◽

Signal To Noise Ratio ◽

Assistive Devices ◽

Frequency Resolution ◽

Noisy Environment ◽

Temporal Synchrony ◽

Reduced Frequency ◽

Useful Signal ◽

Negative Impacts ◽

Target Speaker

Those experiencing hearing loss face severe challenges in perceiving speech in noisy situations such as a busy restaurant or cafe. There are many factors contributing to this deficit including decreased audibility, reduced frequency resolution, and decline in temporal synchrony across the auditory system. Some hearing assistive devices implement beamforming in which multiple microphones are used in combination to attenuate surrounding noise while the target speaker is left unattenuated. In increasingly challenging auditory environments, more complex beamforming algorithms are required, which increases the processing time needed to provide a useful signal-to-noise ratio of the target speech. This study investigated whether the benefits from signal enhancement from beamforming are outweighed by the negative impacts on perception from an increase in latency between the direct acoustic signal and the digitally enhanced signal. The hypothesis for this study is that an increase in latency between the two identical speech signals would decrease intelligibility of the speech signal. Using 3 gain / latency pairs from a beamforming simulation previously completed in lab, perceptual thresholds of SNR from a simulated use case were obtained from normal hearing participants. No significant differences were detected between the 3 conditions. When presented with 2 copies of the same speech signal presented at varying gain / latency pairs in a noisy environment, any negative intelligibility effects from latency are masked by the noise. These results allow for more lenient restrictions for limiting processing delays in hearing assistive devices.

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Pitch Detection Based on Signal-to-Noise-Ratio Estimation and Compensation for Continuous Speech Signal

Convergence and Hybrid Information Technology - Communications in Computer and Information Science ◽

10.1007/978-3-642-32692-9_98 ◽

2012 ◽

pp. 767-774 ◽

Cited By ~ 2

Author(s):

Hyung-Woo Park ◽

A-Ra Khil ◽

Myung-Jin Bae

Keyword(s):

Speech Signal ◽

Signal To Noise Ratio ◽

Pitch Detection ◽

Ratio Estimation ◽

Continuous Speech ◽

Signal To Noise ◽

Noise Ratio

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Improving the signal‐to‐noise‐ratio of a noisy speech signal

The Journal of the Acoustical Society of America ◽

10.1121/1.2004888 ◽

1980 ◽

Vol 68 (S1) ◽

pp. S71-S71

Author(s):

M. M. Sondhi ◽

C. E. Schmidt ◽

L. R. Rabiner

Keyword(s):

Speech Signal ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Noisy Speech ◽

Noise Ratio

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Empirical Mode Decomposition and Robust Pitch Detection Based on Recurrence Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.1035 ◽

2013 ◽

Vol 303-306 ◽

pp. 1035-1038

Author(s):

Jing Fang Wang

Keyword(s):

Empirical Mode Decomposition ◽

Pitch Detection ◽

Intrinsic Mode Functions ◽

Noisy Environment ◽

Method Performance ◽

Recurrence Analysis ◽

Hilbert Huang Transform ◽

Mode Decomposition ◽

Band Filter ◽

Better Than

A new pitch detection method is designed by the recurrence analysis in this paper, which is combined of Empirical Mode Decomposition (EMD) and Elliptic Filter (EF). The Empirical Mode Decomposition (EMD) of Hilbert-Huang Transform (HHT) are utilized tosolve the problem, and a noisy voice is first filtered on the elliptic band filter. The two Intrinsic Mode Functions (IMF) are synthesized by EMD with maximum correlation of voice, and then the pitch be easily divided. The results show that the new method performance is better than the conventional autocorrelation algorithm and cepstrum method, especially in the part that the surd and the sonant are not evident, and get a high robustness in noisy environment.

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Speech Enhancement based on Wiener Filter and Compressive Sensing

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v2.i2.pp367-379 ◽

2016 ◽

Vol 2 (2) ◽

pp. 367

Author(s):

Amart Sulong ◽

Teddy Surya Gunawan ◽

Mira Kartiwi

Keyword(s):

Compressive Sensing ◽

Speech Enhancement ◽

Speech Signal ◽

Signal To Noise Ratio ◽

Algorithm Design ◽

Wiener Filter ◽

Noise Signal ◽

High Signal ◽

Noisy Speech ◽

Performance Results

In communication medium to satisfy the speech enhancement process by using differents methodologies and algoirthms are the key term in testing the system design well enough to produce the best performance results for the speech system. The Wiener filter is one of the classical algorithm that applied to speech process to avoid the noise attacking the speech signal. In other word, compressive sensing method by randomize measurement matrix are combined with the Wiener filter to analyse the noisy speech signal with less introduce to noise signal and producing high signal to noise ratio. The PESQ is used to measure the quality of the proposed algorithm design. As in the experimental results shows that, attacking of defferent noise environments in speech signal still effectively improve the performance of noisy speech with maintain the high score of the PESQ quality.

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Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180511151646 ◽

2019 ◽

Vol 12 (2) ◽

pp. 138-151

Author(s):

Mourad Talbi ◽

Med Salim Bouhlel

Keyword(s):

Wavelet Transform ◽

Speech Signal ◽

Signal To Noise Ratio ◽

Perceptual Quality ◽

Lifting Wavelet Transform ◽

Signal To Noise ◽

Perceptual Evaluation ◽

Lifting Wavelet ◽

Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

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