Blind separation of underwater acoustic signals
Summary (2 min read)
I. INTRODUCTION
- To estimate ocean physical parameters (such as temperature distribution, currents, sediment structure), The Ocean Acoustic Tomography (OAT) is widely used.
- In active OAT, a typical sound source in known fixed position should be emitted.
- These methods can be applied in many fields including speech processing, data communication, biomedical signal processing, radar, sonar as well as the surveillance and control of airport and sea traffic.
- The authors apply BSS algorithms in PAT in order to improve and simplify the PAT algorithms as well as the processing of the received signals.
A. Acoustic Channel Model
- Under some mild assumptions [12] , acoustic submarine channel can be considered as a multiple paths which, in frequency domain, each of them can be defined by a complex constant gain (i.e. a real lag i τ and a real gain C i ).
- In the following, the authors assume that the channel is a linear and causal one and that the coefficients hij(z) are FIR filters.
- The instantaneous case has been studied in [1] .
- -After that, the same algorithm should be tested on simple mixture of acoustic signals.
- In practise, their simulated underwater acoustic channel used the ray theory as a propagation model which is the more appropriate model to their application.
B. Acoustic Signals
- Generally, Independent Component Analysis (ICA) algorithms use only the independence assumption of the sources.
- In PAT applications, the sources are some signals of opportunities.
- That study is of extreme important to ours.
- In fact, according to the characterization study, one can conclude the following facts: -All the recorded signals have a background ocean noise which can be considered as an Additive White Gaussian Noise (AWGN).
- -Many signals are Gaussians or close to Gaussians ones.
A. Background and Assumptions
- The selected algorithms have been tested using the following three steps: -To valid their implementation, the authors use same (or similar) signals used by the authors of the algorithm, and they try to obtain same (or similar) results shown by the authors.
- In their experimental studies, the authors found that at the third step none of the tested algorithms can unfortunately achieve a satisfactory separation according to a set of performance indexes [16] .
- For this reason, a complete separation structure has been implemented using the following pre-and postprocessing modules of the signals see Fig.
- -The filter bank is to improve the frequency resolution of the frequency algorithms.
- In [2] , the authors used the following least-squares based joint diagonalization criterion for the case when a sample estimate of each is available: ) , ( m.
C. Convolutive Blind Separation of Non-Stationary
- This algorithm will be called later "Parra".
- The main difference between the two approaches remains on the considered estimation model.
- The authors prefer instead of estimating a forward model B of H and finding a stable inverse to directly estimate a stable multi-path backward FIR model W.
- They wish to find model sources with cross-powerspectral-density satisfying: EQUATION.
IV. EXPERIMENTAL RESULTS
- In almost all the simulations, the separation of artificial or natural mixtures have been successfully achieved.
- The authors should mention here, that good results have been obtained by only applying SOS algorithm except for some configurations notably when the sources are close to the water surface.
- This index is forced to be zero for the mixture values and 1 for the sources.
V. CONCLUSION
- The authors presented a general structure using BSS algorithms applied on a real word application which is the Passive Acoustic Tomography (PAT).
- After many simulations, the authors obtained experimental results that showed the necessity of considering pre-processing and post processing which have to be applied to the observed signals in order to achieve properly the separation of the sources.
- Many algorithms have been implemented and tested on their application but only few BSS algorithms dedicated to the separation of non-stationary signals gave satisfactory results.
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Citations
299 citations
Cites background from "Blind separation of underwater acou..."
...In these situations the sources are the desired signals, yet only the recordings of the mixed sources are available and the mixing process is unknown....
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Cites background from "Blind separation of underwater acou..."
...BSS is flourishing in numerous fields, including underwater signal processing [31], communication [27], speech enhancement [37], biomedical [14] and audio signal recognitions [42]....
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Cites background from "Blind separation of underwater acou..."
...Wide applications of CBSS, in speech, music, underwater signals recorded in passive sonar, radio communications, antenna arrays, astronomical data, satellite images and interpret functional brain imaging data (Hansen and Dyrholm, 2003; Mansour et al., 2006; Pedersen et al., 2007)....
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References
8,522 citations
"Blind separation of underwater acou..." refers background in this paper
...It was proved that the output signals can be the sources up to a factor (or filter) scale and up to a permutation [7]....
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2,851 citations
"Blind separation of underwater acou..." refers methods in this paper
...The first stage employs joint diagonalization [3, 4, 5] of the set of cross power spectral density matrices...
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2,721 citations
"Blind separation of underwater acou..." refers methods in this paper
...The first stage employs joint diagonalization [3, 4, 5] of the set of cross power spectral density matrices...
[...]
1,344 citations
875 citations
"Blind separation of underwater acou..." refers background or methods in this paper
...For the latter cases, we found that the Parra algorithm before SOS algorithm could improve the overall results....
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...Using these pre- and post-processings, we found that among the tested algorithms, only two [2, 7] have given satisfactory results....
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...This algorithm will be called later “Parra”....
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...C. Convolutive Blind Separation of Non-Stationary Sources (Parra) The main idea of the algorithm [7] proposed by L. Parra and C. Spence is similar to the previous ones [2] and [8]....
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...The main idea of the algorithm [7] proposed by L....
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