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Fundamental frequency detection method based on improved experience wavelet transformation

An empirical wavelet and detection method technology, applied in speech analysis, instruments, etc., can solve problems such as low accuracy of results, inability to achieve simultaneous application of high-pitched and low-pitched voices, and difficulty in implementation, and achieve high time resolution and accuracy. Effect

Inactive Publication Date: 2017-11-03
NANJING UNIV OF SCI & TECH
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Problems solved by technology

The disadvantage of this method is that it is very difficult to implement when the fundamental frequency changes rapidly, and it cannot be applied to high-pitched and low-pitched speech at the same time. In addition, if there is a large noise in the speech, the accuracy of the obtained results is not high.

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  • Fundamental frequency detection method based on improved experience wavelet transformation
  • Fundamental frequency detection method based on improved experience wavelet transformation
  • Fundamental frequency detection method based on improved experience wavelet transformation

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Embodiment 1

[0044] 1. Taking the test signal "base jī" as an example, its time domain waveform is as follows image 3 As shown, by calculating the short-term energy and zero-crossing rate, the signal is segmented by double-threshold detection, and the segmented signal is passed through a filter of 50 Hz to 1500 Hz. The result is as follows Figure 4 shown;

[0045] 2. Perform Fourier transform on the signal to obtain the spectrum, the result is as follows Figure 5 shown;

[0046] 3. Perform Top-hat transformation on the spectrum of the signal, and detect the envelope of the spectrum. The result is as follows Image 6 shown;

[0047] 4. Use the method of local minimum and maximum to detect the peak value of the spectrum envelope, and divide the area where there is a peak between every two valleys, and obtain the spectrum division scheme of the original signal. The result is as follows Figure 7 shown;

[0048] 5. Constructing empirical wavelet basis functions for each spectrum segmen...

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Abstract

The invention discloses a fundamental frequency detection method based on improved experience wavelet transformation. The method comprises steps of step 1, preprocessing a speech signal: calculating short-term energy and zero-crossing rate of the speech signal, using a dual-threshold method to carry out rhythm segmentation and passing the segmented signal through a 50-1500Hz band-pass filter for filtering so as to obtain pre-processed speech signal; step 2, using the improved experience wavelet transformation method to decompose the preprocessed speech signal so as to obtain each mode function of the speech signal; step 3, according to the mode function, selecting the main mode of the speech signal; step 4, using Hilbert transformation to solve a transient fundamental frequency value of the main mode; and step 5, using a rectangular window function to carry smoothing processing on the transient fundamental frequency value obtained in the step 4 to finish the fundamental frequency detection. The method is characterized by high accuracy, quite good robustness and high time resolution.

Description

technical field [0001] The invention belongs to the field of speech signal analysis and processing, and proposes a complete set of fundamental frequency detection algorithms based on improved empirical wavelet transform. Background technique [0002] Speech signals have non-stationary and nonlinear characteristics. Common methods for studying non-stationary signals include windowed Fourier transform, continuous wavelet transform, and empirical mode decomposition (EMD). The commonly used methods for fundamental frequency detection of speech signals are divided into frame-based detection and event-based detection. Frame-based detection determines the fundamental frequency by computing the average period of a segment of the speech signal. It is assumed that the speech signal is a stationary signal in a certain period, and there are sampling points above two pitch periods. The disadvantage of this method is that it is very difficult to implement when the fundamental frequency ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L25/27
CPCG10L25/27
Inventor 李彧晟薛彪洪弘顾陈朱晓华
Owner NANJING UNIV OF SCI & TECH
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