CN108680900A - A kind of ESPRIT sound localization methods based on RANSAC - Google Patents
A kind of ESPRIT sound localization methods based on RANSAC Download PDFInfo
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- CN108680900A CN108680900A CN201810486052.2A CN201810486052A CN108680900A CN 108680900 A CN108680900 A CN 108680900A CN 201810486052 A CN201810486052 A CN 201810486052A CN 108680900 A CN108680900 A CN 108680900A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
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Abstract
The invention discloses a kind of, and the ESPRIT sound localization methods based on RANSAC obtain a frame voice signal X first to one section of voice signal X (t) progress sub-frame processingw(t)；To Xw(t) being decomposed into two has invariable rotary characteristic submatrix, the first submatrix signal Xw1(t) and the second submatrix signal Xw2(t), and data covariance matrix R is calculated；Then Eigenvalues Decomposition, the signal subspace U that the big characteristic vector to obtain the first submatrix is turned into are carried out to data covariance matrixs1The signal subspace U being turned into the big characteristic vector of the second submatrixs2；The constant matrix Ψ of the rotation relationship containing target signal direction position is obtained followed by signal subspace；Then matrix Ψ constant to rotation relationship carries out Eigenvalues Decomposition and obtains K characteristic value, the location parameter of corresponding K signal；Finally after obtaining the sound source position parameter of each frame, the corrupt data in these parameters is eliminated, an optimal sample set is obtained and then fits accurate location parameter.The present invention can obtain more accurate positional parameter.
Description
Technical field
The invention belongs to auditory localization technical fields, are related to a kind of sound localization method, and in particular to one kind is based on
The ESPRIT sound localization methods of RANSAC.
Background technology
Based on microphone array acoustic model, the direction of arrival (DOA) of voice signal can be by formula X (t)=AS (t)+N
(t) it obtains.Wherein A is the array manifold of signal, and S (t) is echo signal, and N (t) is noise signal.ESPRIT algorithms hypothesis is made an uproar
Acoustical signal N (t) is to be received and due to the complexity of communication environments in actual conditions with the incoherent Gaussian noise of echo signal
Be in signal have existing for coherent signal source, including co-channel interference and due to background object reflect caused by multipath-propagated signal,
The accuracy of ESPRIT algorithms can be caused to decline at this time.
Invention content
In order to solve the above technical problem, the present invention provides a kind of ESPRIT sound localization methods based on RANSAC,
The fitting effect for capableing of positional parameter by this method is more preferable, improves accuracy and the robustness of ESPRIT location algorithms.
The technical solution adopted in the present invention is：A kind of ESPRIT sound localization methods based on RANSAC, feature exist
In including the following steps：
Step 1：Sub-frame processing X is carried out by window function w (t) to one section of voice signal X (t)w(t)=X (t) * w (t), obtain
To a frame voice signal Xw(t)；
Step 2：To Xw(t) two submatrix signals with invariable rotary characteristic are decomposed into, wherein the first submatrix signal is remembered
For Xw1(t), the second submatrix signal is denoted as Xw2(t), and data covariance matrix R is calculated；
Step 3：Eigenvalues Decomposition is carried out to data covariance matrix, the big characteristic vector to obtain the first submatrix is turned into
Signal subspace Us1The signal subspace U being turned into the big characteristic vector of the second submatrixs2；
Step 4：The constant matrix of the rotation relationship containing target signal direction position is obtained using signal subspace
Step 5：Matrix Ψ constant to rotation relationship carries out Eigenvalues Decomposition and obtains K characteristic value, the position of corresponding K signal
Set parameter；
Step 6：After obtaining the sound source position parameter of each frame, eliminated in these parameters not using RANSAC algorithms
Authentic data obtains an optimal sample set and then fits accurate location parameter.
Influence present invention reduces coherent to positioning.Removed in position fixing process due to noise by RANSAC algorithms
The unreliable point generated so that the fitting effect of positional parameter is more preferable, improves accuracy and the robustness of ESPRIT location algorithms.
Description of the drawings
Fig. 1 is the flow chart of the embodiment of the present invention；
Fig. 2 is the experimental result comparison diagram of the embodiment of the present invention.
Specific implementation mode
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Referring to Fig.1, a kind of ESPRIT sound localization methods based on RANSAC provided by the invention, include the following steps：
Step 1：Sub-frame processing X is carried out by window function w (t) to one section of voice signal X (t)w(t)=X (t) * w (t), obtain
To a frame voice signal Xw(t)；
Step 2：To Xw(t) two submatrix signals with invariable rotary characteristic are decomposed into, wherein the first submatrix signal is remembered
For Xw1(t), the second submatrix signal is denoted as Xw2(t), and data covariance matrix R is calculated；
Step 3：Eigenvalues Decomposition is carried out to data covariance matrix, the big characteristic vector to obtain the first submatrix is turned into
Signal subspace Us1The signal subspace U being turned into the big characteristic vector of the second submatrixs2；
Step 4：The constant matrix of the rotation relationship containing target signal direction position is obtained using signal subspace
In the present embodiment, Xw(t) the submatrix signal X that there is invariable rotary characteristic with twow1(t) and Xw2(t) pass between
System is：
Data covariance matrix R is：
R=E [Xw(t)Xw(t)H] (2)
Eigenvalues Decomposition is carried out to data covariance matrix and obtains the signal subspace U of two data matrixess1And Us2：
In formula, UsFor the signal subspace that the corresponding characteristic vector of big characteristic value is turned into, ∑sTo include the spy of big characteristic value
Value indicative diagonal matrix, UNFor the noise subspace that the corresponding characteristic vector of small characteristic value is turned into, ∑NTo be corresponded to comprising small characteristic value
Characteristic value diagonal matrix；
Calculate the constant matrix Ψ of rotation relationship：
Step 5：Matrix Ψ constant to rotation relationship carries out Eigenvalues Decomposition and obtains K characteristic value, the position of corresponding K signal
Set parameter；
Step 6：After obtaining the sound source position parameter of each frame, eliminated in these parameters not using RANSAC algorithms
Authentic data obtains an optimal sample set and then fits accurate location parameter.
The wherein described RANSAC algorithms, including following sub-step：
Step 6.1：Select point set V of the N frame alignment parameter as RANSAC iteration；
Step 6.2：N value is randomly selected in V fits a preliminary sound source incident angleAnd remaining N-n
Incident angle is θi；
Step 6.3：Assuming that error threshold is ε, a consistency set V is definedk, willθiIt is put into consistent
Property set, and record VkIn data amount check；
Step 6.4：Assuming that VkIn data amount check and V in data amount check ratio be this calculating accuracy δk, when
δkWhen less than preset model accuracy δ, step 6.2 and step 6.3 are repeated；If working as δkIt is correct more than preset model
When rate δ, by consistency set Vk'sAs optimal solution；
Step 6.5：When obtaining most homogeneous set V in step 6.4kAfterwards, optimal solution is found out using least square method.
It is the experimental result comparison diagram of the present embodiment see Fig. 2, comparison algorithm improves front and back as signal-to-noise ratio (SNR) becomes
The root-mean-square error (RMSE) of change, as shown in Fig. 2, when signal-to-noise ratio is reduced to -20dB, algorithm is still kept preferable after improvement
Positioning accuracy, and algorithm produces larger error before improving.Illustrate that algorithm has higher precision and stronger after improving
Noise immunity.
The auditory localization of ESPRIT algorithms can be removed in the process due to the corrupt data that noise generates by this method, obtained
To more accurate positional parameter.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention
Profit requires under protected ambit, can also make replacement or deformation, each fall within protection scope of the present invention, this hair
It is bright range is claimed to be determined by the appended claims.
Claims (3)
1. a kind of ESPRIT sound localization methods based on RANSAC, which is characterized in that include the following steps：
Step 1：Sub-frame processing X is carried out by window function w (t) to one section of voice signal X (t)w(t)=X (t) * w (t), obtain one
Frame voice signal Xw(t)；
Step 2：To Xw(t) two submatrix signals with invariable rotary characteristic are decomposed into, wherein the first submatrix signal is denoted as Xw1
(t), the second submatrix signal is denoted as Xw2(t), and data covariance matrix R is calculated；
Step 3：Eigenvalues Decomposition, the letter that the big characteristic vector to obtain the first submatrix is turned into are carried out to data covariance matrix
Work song space Us1The signal subspace U being turned into the big characteristic vector of the second submatrixs2；
Step 4：The constant matrix of the rotation relationship containing target signal direction position is obtained using signal subspace
Step 5：Matrix Ψ constant to rotation relationship carries out Eigenvalues Decomposition and obtains K characteristic value, the location parameter of corresponding K signal；
Step 6：After obtaining the sound source position parameter of each frame, it is unreliable in these parameters to be eliminated using RANSAC algorithms
Data obtain an optimal sample set and then fit accurate location parameter.
2. the ESPRIT sound localization methods according to claim 1 based on RANSAC, which is characterized in that Xw1(t) and Xw2
(t) relationship between is：
Data covariance matrix R is：
R=E [Xw(t)Xw(t)H] (2)
Eigenvalues Decomposition is carried out to data covariance matrix and obtains the signal subspace U of two data matrixess1And Us2：
In formula, UsFor the signal subspace that the corresponding characteristic vector of big characteristic value is turned into, ∑sTo include the characteristic value of big characteristic value
Diagonal matrix, UNFor the noise subspace that the corresponding characteristic vector of small characteristic value is turned into, ∑NTo include the corresponding spy of small characteristic value
Value indicative diagonal matrix；
Calculate the constant matrix Ψ of rotation relationship：
3. the ESPRIT sound localization methods according to claim 1 based on RANSAC, which is characterized in that institute in step 6
State RANSAC algorithms, including following sub-step：
Step 6.1：Select point set V of the N frame alignment parameter as RANSAC iteration；
Step 6.2：N value is randomly selected in V fits a preliminary sound source incident angleAnd remaining N-n incident
Angle is θi；
Step 6.3：Assuming that error threshold is ε, a consistency set V is definedk, willθiIt is put into consistency collection
It closes, and records VkIn data amount check；
Step 6.4：Assuming that VkIn data amount check and V in data amount check ratio be this calculating accuracy δk, work as δkIt is small
When preset model accuracy δ, step 6.2 and step 6.3 are repeated；If working as δkMore than preset model accuracy δ
When, by consistency set Vk'sAs optimal solution；
Step 6.5：When obtaining most homogeneous set V in step 6.4kAfterwards, the set is fitted using least square method
Find out optimal solution.
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| CN105894000A (en) * | 2016-03-28 | 2016-08-24 | 江南大学 | RANSAC-based laser network mark image feature extraction |
| CN108401565B (en) * | 2015-05-28 | 2017-12-15 | 西北工业大学 | Remote sensing image registration method based on improved KAZE features and Pseudo-RANSAC algorithms |
-
2018
- 2018-05-21 CN CN201810486052.2A patent/CN108680900A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102879764A (en) * | 2012-10-16 | 2013-01-16 | 浙江大学 | Underwater sound source direction estimating method |
| CN104766319A (en) * | 2015-04-02 | 2015-07-08 | 西安电子科技大学 | Method for improving registration precision of images photographed at night |
| CN108401565B (en) * | 2015-05-28 | 2017-12-15 | 西北工业大学 | Remote sensing image registration method based on improved KAZE features and Pseudo-RANSAC algorithms |
| CN105894000A (en) * | 2016-03-28 | 2016-08-24 | 江南大学 | RANSAC-based laser network mark image feature extraction |
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| SYLVESTER0510: "RANSAC介绍（Matlab版直线拟合+平面拟合）", 《HTTPS://BLOG.CSDN.NET/U010128736/ARTICLE/DETAILS/53422070?SPM=1001.2014.3001.5501》 * |
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