CN108304855A - More submarine characteristic signal blind source separation methods under a kind of marine environment - Google Patents
More submarine characteristic signal blind source separation methods under a kind of marine environment Download PDFInfo
- Publication number
- CN108304855A CN108304855A CN201711267293.XA CN201711267293A CN108304855A CN 108304855 A CN108304855 A CN 108304855A CN 201711267293 A CN201711267293 A CN 201711267293A CN 108304855 A CN108304855 A CN 108304855A
- Authority
- CN
- China
- Prior art keywords
- cluster
- mask
- clustering
- signal
- submarine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000926 separation method Methods 0.000 title claims abstract description 26
- 239000013598 vector Substances 0.000 claims abstract description 10
- 238000003064 k means clustering Methods 0.000 claims abstract description 5
- 238000012795 verification Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 4
- 230000002596 correlated effect Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 abstract description 6
- 241000283153 Cetacea Species 0.000 description 15
- 238000010586 diagram Methods 0.000 description 5
- 241000283084 Balaenoptera musculus Species 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 241000283323 Delphinapterus leucas Species 0.000 description 2
- 108010059642 isinglass Proteins 0.000 description 2
- 238000000513 principal component analysis Methods 0.000 description 2
- 230000005236 sound signal Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
- Obtaining Desirable Characteristics In Audible-Bandwidth Transducers (AREA)
Abstract
The invention discloses more submarine characteristic signal blind source separation methods under a kind of marine environment, include the following steps：Acquire sample of signal；Sample vector based on the angles Hermitian in time-frequency domain and reference vector use clustering algorithm to be clustered to mixed signal sample to obtain multiple clustering clusters, using the membership function in clustering algorithm as mask；Cluster verification is carried out based on estimation number of clusters mesh；Mask is clustered into Q mask clustering cluster based on K means clustering algorithms；By mask estimation, Signal separator is carried out.The present invention can solve the problems, such as blind source separating in the case of owing fixed, and any mixed signal matrix or source position need not be estimated in mask estimation, the complexity of Signal separator is simplified, improves operation efficiency.
Description
Technical Field
The invention relates to the field of underwater acoustic signal processing, in particular to a method and a system for separating blind sources of multi-submarine characteristic signals in a marine environment.
Background
The current blind separation algorithm has many factor limitations, firstly, the blind separation algorithm is mainly used in a transient mixed form of signals, and the blind separation algorithms in a signal convolution mixed form are relatively few and can be roughly divided into the following categories: 1) the network solves the problem by using a feedback circuit based on a signal separation network that eliminates cross-correlation between signal components. The algorithm has a great defect in practical application, and has a poor separation effect even under the condition that the scale difference between signals is large or a mixed matrix is pathological, so that the signals cannot be separated. 2) The output is transformed by a nonlinear transfer function, such as a entropy maximization method based on the principle of information transmission maximization. The algorithm has a low convergence rate, and the inversion of the separation matrix brings unstable values. 3) The nonlinear Principal Component Analysis (PCA) algorithm, which is a generalization of the linear principal component analysis method. In addition, the above algorithm needs to estimate any mixed signal matrix or source position in mask estimation, so that the signal separation process is relatively complex and the operation efficiency is not high.
Disclosure of Invention
The invention aims to provide a multi-submarine characteristic signal blind source separation method in a marine environment, which can solve the problem of blind source separation under an underdetermined condition, simplifies the complexity of signal separation and improves the operation efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-submarine characteristic signal blind source separation method in a marine environment comprises the following steps:
s1, collecting a mixed signal sample;
s2, clustering the mixed signal samples by adopting a clustering algorithm to obtain a plurality of clustering clusters based on Hermitian angle sample vectors and reference vectors in a time-frequency domain, and taking membership functions in the clustering algorithm as masks;
s3, performing cluster verification based on the estimated cluster number;
s4, clustering the masks into Q mask cluster based on K-means clustering algorithm, and recording as cqQ is 1.. Q, and satisfies DqQ1.. the sum of Q is minimal, DqIs the total distance of the mask from the cluster centroid in the qth cluster, i.e.:
wherein,is the ith mask, C, of the k frequency pointqIs the qth cluster cqThe center of mass of the magnetic field sensor,is thatAnd cluster centroid CqPearson's correlation coefficient between, kstAnd kendAre the start and end frequency points of the adjacent frequency point groups used for clustering, i.e. the total number of frequency points is kend-kst+1，As a distance measure, such highly correlated masks (smaller distances) will come from one cluster;
s5, signal separation is performed by mask estimation.
Preferably, the clustering algorithm in step S2 adopts a K-means clustering or fuzzy C-means clustering algorithm.
After adopting the technical scheme, compared with the background technology, the invention has the following advantages:
the method is based on the sample vector and the reference vector of the Hermitian angle, the mixed signal samples are gathered by using a clustering algorithm, the unknown signals are separated, the problem of blind source separation under the underdetermined condition can be solved, any mixed signal matrix or source position does not need to be estimated in mask estimation, the complexity of signal separation is simplified, and the operation efficiency is improved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of the arrangement of source signals and receivers;
FIG. 3 is a schematic diagram of a channel model;
FIG. 4a shows the original acoustic signal waveform of a whale, and FIG. 4b shows the original acoustic signal waveform of a whale;
FIG. 5a shows the original sound spectrogram of beluga, and FIG. 5b shows the original sound spectrogram of Ontario whale;
fig. 6a shows a mixed sound waveform captured by the receiving terminal R1, and fig. 6b shows a mixed sound waveform captured by the receiving terminal R2;
FIG. 7a is a waveform of an isolated whale sound and FIG. 7b is a waveform of an isolated whale sound;
fig. 8a is a sound spectrogram of an isolated whale, and fig. 8b is a sound spectrogram of an isolated whale.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example one
Referring to fig. 1, the invention discloses a method for separating blind sources of multi-submarine characteristic signals in a marine environment, comprising the following steps:
and S1, collecting mixed signal samples.
And S2, clustering the mixed signal samples by adopting a clustering algorithm to obtain a plurality of clustering clusters based on a Hermitian (Hermitian matrix) angle sample vector and a reference vector in a time-frequency domain, and taking a membership function in the clustering algorithm as a mask. The clustering algorithm adopts a K-mean clustering algorithm or a fuzzy C-mean clustering algorithm.
And S3, performing cluster verification based on the estimated cluster number.
S4, clustering the masks into Q mask cluster based on K-means clustering algorithm, and recording as cqQ is 1.. Q, and satisfies DqQ1.. the sum of Q is minimal, DqIs the total distance of the mask from the cluster centroid in the qth cluster, i.e.:
wherein,is the ith mask, C, of the k frequency pointqIs the qth cluster cqThe center of mass of the magnetic field sensor,is thatAnd cluster centroid CqBetweenPearson's correlation coefficient, kstAnd kendAre the start and end frequency points of the adjacent frequency point groups used for clustering, i.e. the total number of frequency points is kend-kst+1，As a distance measure, such highly correlated masks (smaller distances) will come from one cluster.
S5, signal separation is performed by mask estimation.
Evaluation of experiments
One, channel design
Two signal receiving ends (R1 and R2) and two sound signal emitting ends (S1 and S2) are simulated underwater at any positions. Wherein R1 is 3.5km from S1, R2 is 4.0km from S2, R1 is 4.5km from S2, and R2 is 4.7km from S1, the arrangement is shown in FIG. 2.
R1 and S1 form channel h11, R1 and S2 form channel h12, R2 and S1 form channel h21, R2 and S2 form channel h22, and four channel models are shown in fig. 3.
Second, simulation experiment
The simulation experiment is carried out on a Matlab2014b platform, the processor of the computer is AMD dual-core A6-4400M2.7GHz, and the memory is 4G.
Sounds used in the simulation experiment are the sounds of blue whales and standing whales respectively, and a sound separation experiment for different types of whales and a sound separation experiment for two-head blue whales are performed respectively.
FIG. 4a shows the original acoustic signal waveform of a whale, and FIG. 4b shows the original acoustic signal waveform of a whale; FIG. 5a shows the original sound spectrogram of beluga, and FIG. 5b shows the original sound spectrogram of Ontario whale; fig. 6a shows a mixed sound waveform captured by the receiving terminal R1, and fig. 6b shows a mixed sound waveform captured by the receiving terminal R2.
Third, analysis of separation results
The mixed sound signal is separated according to the blind source separation method of the present invention, and the separated signal waveforms are shown in fig. 7, wherein fig. 7a is a separated blue whale sound waveform, and fig. 7b is a separated whale sound waveform. Comparing fig. 7a with fig. 4a, and comparing fig. 7b with fig. 4b, it can be seen that the blind source separation method of the present invention can better separate the mixed signals.
For further examination, the separated signal was subjected to Short Time Fourier Transform (STFT) to obtain a spectrum image as shown in fig. 8, where fig. 8a is a spectrum diagram of a separated whale sound and fig. 8b is a spectrum diagram of a separated whale sound. Comparing fig. 8a with fig. 5a, the characteristic energies at the bottom of the two are consistent; comparing fig. 8b with fig. 5b, the characteristic energies at the bottom of both are consistent.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A multi-submarine characteristic signal blind source separation method in a marine environment is characterized by comprising the following steps:
s1, collecting a mixed signal sample;
s2, clustering the mixed signal samples by adopting a clustering algorithm to obtain a plurality of clustering clusters based on Hermitian angle sample vectors and reference vectors in a time-frequency domain, and taking membership functions in the clustering algorithm as masks;
s3, performing cluster verification based on the estimated cluster number;
s4, calculating based on K-means clusteringThe method clusters the masks into Q mask cluster, which is marked as cqQ is 1.. Q, and satisfies DqQ1.. the sum of Q is minimal, DqIs the total distance of the mask from the cluster centroid in the qth cluster, i.e.:
wherein,is the ith mask, C, of the k frequency pointqIs the qth cluster cqThe center of mass of the magnetic field sensor,is thatAnd cluster centroid CqPearson's correlation coefficient between, kstAnd kendAre the start and end frequency points of the adjacent frequency point groups used for clustering, i.e. the total number of frequency points is kend-kst+1，As a distance measure, such highly correlated masks (smaller distances) will come from one cluster;
s5, signal separation is performed by mask estimation.
2. The method for blind source separation of multi-submarine signatures according to claim 1, wherein: the clustering algorithm in step S2 adopts a K-means clustering or fuzzy C-means clustering algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711267293.XA CN108304855B (en) | 2017-12-05 | 2017-12-05 | Multi-submarine characteristic signal blind source separation method in marine environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711267293.XA CN108304855B (en) | 2017-12-05 | 2017-12-05 | Multi-submarine characteristic signal blind source separation method in marine environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108304855A true CN108304855A (en) | 2018-07-20 |
| CN108304855B CN108304855B (en) | 2020-06-23 |
Family
ID=62870258
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711267293.XA Active CN108304855B (en) | 2017-12-05 | 2017-12-05 | Multi-submarine characteristic signal blind source separation method in marine environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108304855B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112133321A (en) * | 2020-09-23 | 2020-12-25 | 青岛科技大学 | Underwater acoustic signal Gaussian/non-Gaussian noise suppression method based on blind source separation |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105282067A (en) * | 2015-09-16 | 2016-01-27 | 长安大学 | Complex field blind source separation method |
-
2017
- 2017-12-05 CN CN201711267293.XA patent/CN108304855B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105282067A (en) * | 2015-09-16 | 2016-01-27 | 长安大学 | Complex field blind source separation method |
Non-Patent Citations (2)
| Title |
|---|
| 张良俊: "欠定盲源分离算法及其应用研究", 《中国优秀硕士学位论文全文数据库 (基础科学辑)》 * |
| 郭鹿鸣: "基于盲源分离的心肺音信号分离方法研究与应用", 《中国优秀硕士学位论文全文数据库 (基础科学辑)》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112133321A (en) * | 2020-09-23 | 2020-12-25 | 青岛科技大学 | Underwater acoustic signal Gaussian/non-Gaussian noise suppression method based on blind source separation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108304855B (en) | 2020-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Deep attractor network for single-microphone speaker separation | |
| Li et al. | Multiple-speaker localization based on direct-path features and likelihood maximization with spatial sparsity regularization | |
| Kounades-Bastian et al. | A variational EM algorithm for the separation of time-varying convolutive audio mixtures | |
| Wang et al. | A region-growing permutation alignment approach in frequency-domain blind source separation of speech mixtures | |
| Wang et al. | Over-determined source separation and localization using distributed microphones | |
| Yang et al. | Under-determined convolutive blind source separation combining density-based clustering and sparse reconstruction in time-frequency domain | |
| Sundar et al. | TDOA-based multiple acoustic source localization without association ambiguity | |
| CN108091345B (en) | Double-ear voice separation method based on support vector machine | |
| Opochinsky et al. | Deep ranking-based sound source localization | |
| Traa et al. | Blind multi-channel source separation by circular-linear statistical modeling of phase differences | |
| Mack et al. | Single-Channel Blind Direct-to-Reverberation Ratio Estimation Using Masking. | |
| CN108304855B (en) | Multi-submarine characteristic signal blind source separation method in marine environment | |
| CN110265060B (en) | Speaker number automatic detection method based on density clustering | |
| Casebeer et al. | Deep tensor factorization for spatially-aware scene decomposition | |
| Jafari et al. | Underdetermined blind source separation with fuzzy clustering for arbitrarily arranged sensors | |
| Smaragdis et al. | Learning source trajectories using wrapped-phase hidden Markov models | |
| Seghouane et al. | Estimating the number of significant canonical coordinates | |
| Mitianoudis | Audio source separation using independent component analysis | |
| Kim et al. | Efficient neighborhood-based topic modeling for collaborative audio enhancement on massive crowdsourced recordings | |
| Jafari et al. | Sparse coding for convolutive blind audio source separation | |
| Chen et al. | LOCSELECT: Target Speaker Localization with an Auditory Selective Hearing Mechanism | |
| Dias et al. | Multichannel source separation using time-deconvolutive CNMF | |
| Fakhry et al. | Underdetermined source detection and separation using a normalized multichannel spatial dictionary | |
| Li et al. | Expectation‐maximisation for speech source separation using convolutive transfer function | |
| Korats et al. | Impact of Window Length and Decorrelation Step on ICA Algorithms for EEG Blind Source Separation. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |