Pitch Detection Method Based on HHT

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.

Pitch Detection Method Based on Morphological Filtering

2014 ◽  
Vol 596 ◽  
pp. 433-436 ◽  
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.

Research on Pitch Extraction Algorithm of Noisy Speech

2012 ◽  
Vol 433-440 ◽  
pp. 4675-4678
Author(s):  
Hong Yan Xing ◽  
Cui Hua Yu ◽  
Peng Li
Keyword(s):  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Pitch Detection ◽  
Noisy Environment ◽  
Pitch Period ◽  
Noisy Speech ◽  
Hilbert Huang Transform ◽  
Extraction Algorithm ◽  
Soft Threshold ◽  
Pitch Extraction

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.

Nonlinear modeling and filtering by RBF network with application to noisy speech signal

1997 ◽  
Vol 101 (3-4) ◽  
pp. 177-185 ◽  
Author(s):  
Eiji Uchino ◽  
Shin Nakamura ◽  
Takeshi Yamakawa
Keyword(s):  
Speech Signal ◽  
Nonlinear Modeling ◽  
Noisy Speech ◽  
Rbf Network

scholarly journals Enhancement of the perceptive quality of the noisy speech signal by using of DFF-FBC algorithm

2009 ◽  
Vol 22 (3) ◽  
pp. 391-404
Author(s):  
Zoran Milivojevic ◽  
Dragisa Balaneskovic
Keyword(s):  
Speech Signal ◽  
Mean Opinion Score ◽  
Signal Quality ◽  
Test Results ◽  
Frequency Filtering ◽  
Noisy Speech ◽  
Voice Signal ◽  
Opinion Score

This paper presents an algorithm for enhancement of the noisy speech signal quality. This algorithm is based on the dissonant frequency filtering (DFF), F#, B and C# in relation to the frequency of the primary tone C (DFF-FBC algorithm). By means of the subjective Mean Opinion Score (MOS) test, the effect of the enhancement of the speech signal quality was analyzed. The analysis of the MOS test results, presented in the second part of this paper, points out to the enhancement of the noisy speech signal quality in the presence of superimposed noises. Especially good results have been found with Husky Voice signal. .

scholarly journals EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

2021 ◽  
Vol 11 (4) ◽  
pp. 22-27
Author(s):  
Paweł Kowalski ◽  
Piotr Tojza
Keyword(s):  
Detection Method ◽  
Computational Cost ◽  
New Method ◽  
Line Detection ◽  
Curve Detection ◽  
Real Time Image ◽  
Convolution Filter ◽  
Straight Lines ◽  
Time Image ◽  
Local Detection

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚.  Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

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