Speech Enhancement Method Based on Nonlocal Mean Filtering
A non-local mean, speech enhancement technology, applied in speech analysis, instruments, etc., can solve the problems of affecting hearing quality, difficult to achieve accurate estimation, easy residual noise in the background, etc., to avoid excessive dependence, reduce music noise, and improve clarity. Effect
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2016-11-23
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
Figure 1 Figure 2 Figure 3
Abstract
Description
technical field
[0001] The invention belongs to the technical field of speech processing, specifically based on non-local mean value filtering, using the weighted average of similar signal points in the signal neighborhood to reduce speech noise, and can be used in mobile communication. Background technique
[0002] Speech is a unique function of human beings, and it is also the most important means of transmitting information to each other. Speech in real life is inevitably affected by the surrounding environment. Some strong background noises, such as mechanical noise, voices of other speakers, etc., will seriously affect the quality of the voice signal. In addition, the transmission system itself will also generate various noises, so the signal at the receiving end is a noisy speech signal. The main goal of speech enhancement is to extract pure speech signals from noisy speech as much as possible at the receiving end, reduce the listening fatigue of listeners, and impro...
Examples
Embodiment Construction
[0031] refer to figure 1 , the present invention is based on the speech enhancement method of non-local mean filter, and its realization steps are as follows:
[0032] Step 1, input the noisy speech, and calculate the power spectrum of the noisy speech signal.
[0033] 1.1) For the input noisy speech y(i), divide the noisy speech into N frames with 256 speech points per frame, and overlap 128 points between frames to obtain the frame-divided signal y λ (i 1 ), and for y λ (i 1 ) plus Hamming window to get the windowed signal y λ (i 1 )':
[0034] the y λ (i 1 )'=y λ (i 1 )*ham(256);
[0035] Among them, i represents the time-domain signal discrete point sequence, i=1,2,...,m, m represents the total number of voice sequence numbers, λ represents the number of frame sequences, λ=1,2,...,N, i 1 Indicates the sequence number in the frame, i 1 =1,2,...,256, ham(256) means a Hamming window with a size of 256 points;
[0036] 1.2) For the windowed signal y λ (i 1 )' t...