Frequency-domain Independent Component Analysis Research Articles

A novel method for processing noisy magnetotelluric data based on independence of signal sources and continuity of response functions

Various schemes for processing magnetotelluric (MT) data have been reported aiming at suppressing the strong effect of artificial electromagnetic noises, especially coherent noise that is correlated between electric and magnetic time series. Many of the recent denoising schemes are based on decomposing MT data into the responses of the natural signals and noises. However, a steady differentiation of the natural signals from noises independent of any empirical choice of parameter setting is critically important. In addition, improper subtraction of values from the separated signals can lead to losing useful values of the natural signals or missing noise-affected values, which may result in failure in deriving the true MT responses. We propose a novel data-processing method that applies frequency-domain independent component analysis (FDICA) to both the local MT data and the reference magnetic data. Among the separated signals, the proposed method can quantitatively distinguish the natural signals from the noise-affected components by calculating the ratio of the cross-power spectrum with the reference data to the auto-power spectrum for each component. When determining which values to subtract from the separated signals, we introduce an evaluation index with respect to two characteristics of the MT response function: stationarity in the time domain and smoothness in the frequency domain. We conducted two types of experiments: with MT time series severely contaminated by synthetic coherent noises and with MT field data interfered with direct-current (DC) railways. Consequently, we confirmed the noise-suppression performance superiority of the proposed method over the conventional methods of MT data processing.

A Unifying View on Blind Source Separation of Convolutive Mixtures Based on Independent Component Analysis

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple- N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.

Independent vector analysis based blind interference reduction and signal recovery for MIMO IoT green communications

In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications, frequency domain independent component analysis (FDICA) has been a very popular method but with adverse random permutation ambiguity influence. In order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) based the Internet of Thing (IoT) systems, this paper proposes an new detection mechanism, named independent vector analysis (IVA), for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier interference (ICI) and multiple access interference (MAI). IVA jointly implements separation work for different frequency bin data while the FDICA deals with it separately. In IVA, the dependencies of frequency bins can be exploited in mitigating the random permutation problem. In addition, multivariate prior distributions are employed to preserve the inter-frequency dependencies for individual sources, which can result in separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed method.

Independent Vector Analysis for Blind Deconvolving of Digital Modulated Communication Signals

For the purpose of overcoming the random permutation ambiguity of the frequency-domain-independent component analysis (FDICA) for blind separation of convolutive mixtures, this paper proposes an independent vector analysis (IVA) detection receiver for blindly deconvolving the convolutive mixtures of digitally modulated signals for wireless communications. The foundation of IVA is through jointly carrying out separation work for different frequency bin data fusion, and the dependencies of frequency bins are exploited in solving the random permutation problem of separation signals. In addition, IVA uses multivariate prior distributions instead of the univariate distribution used in FDICA. Multivariate prior distribution is employed to preserve the interfrequency dependencies for individual sources, which can give rise to separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed detection method.

Method for obtaining response functions from noisy magnetotelluric data using frequency-domain independent component analysis

The magnetotelluric (MT) method has been used for visualizing subsurface resistivity structures and more recently for monitoring resistivity changes. However, electromagnetic data often include cultural noise, which can cause errors in the estimation of MT response functions and subsurface resistivity structure analysis. Frequency-domain independent component analysis (FDICA) offers advantages for MT data processing particularly because this method can extract hidden components in the observed data. These components can be decomposed into natural MT signals and cultural noise so that the noise effect in the recovered MT data is reduced. FDICA is applied to MT data acquired at the Kakioka Magnetic Observatory in Japan. The apparent resistivity and phase curves are obtained with small estimated errors between periods of 7 and 12,000 s, although the length of the time-series data is limited. The curves are smoother than those obtained using a conventional method. Various types of synthetic noise are added to the time series at Kakioka to test the noise-reduction performance of FDICA for MT data with high noise contamination. The results demonstrate that FDICA can be used to estimate MT response functions with high accuracy even under conditions in which more than half of the time-series data are contaminated by noise.

Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis

Voice signals acquired by a microphone array often include considerable noise and mutual interference, seriously degrading the accuracy and speed of speech separation. Traditional beamforming is simple to implement, but its source interference suppression is not adequate. In contrast, independent component analysis (ICA) can improve separation, but imposes an iterative and time-consuming process to calculate the separation matrix. As a supporting method, principle component analysis (PCA) contributes to reduce the dimension, retrieve fast results, and disregard false sound sources. Considering the sparsity of frequency components in a mixed signal, we propose an adaptive fast speech separation algorithm based on multiple sound source localization as preprocessing to select between beamforming and frequency domain ICA according to different mixing conditions per frequency bin. First, a fast positioning algorithm allows calculating the maximum number of components per frequency bin of a mixed speech signal to prevent the occurrence of false sound sources. Then, PCA reduces the dimension to adaptively adjust the weight of beamforming and ICA for speech separation. Subsequently, the ICA separation matrix is initialized based on the sound source localization to notably reduce the iteration time and mitigate permutation ambiguity. Simulation and experimental results verify the effectiveness and speedup of the proposed algorithm.

Underwater Source Separation Using Multi-Stage Independent Component Analysis in Semi-Anechoic Water Tank

The experiment of source separation in sea water environment often faced difficulties especially for controlling certain variables. One of the upcoming solution to simplify sea water environment was to conduct the experiment in the anechoic water tank. To evaluate the effects of the experiment performed in the water tank, the comparison of source separation using linear mixture and in-tank mixture will be analyzed. The source separation was performed in three different methods, Time Domain Independent Component Analysis (TDICA), Frequency Domain Independent Component Analysis (FDICA), and Multi Stage Independent Component Analysis (MSICA). The experiment result showing that compared to the linear mixture source, the performance of source separation degrade significantly while performed on the water tank, indicated by the MSE value which increased from 310−8 to 610−2. This result showing that the source mixture that occurred in water tank, much more complex compared to the linear mixture.

Crack Detection of Propeller Shaft on board Marine Ship using Microphone Array

Vibration due to rotation of a shaft of a ship propeller may cause crack due to lack of proper maintenance. It is considered to be hazardous area to conduct a direct measurement on rotating shaft using accelerometer. In this paper, acoustic emission using linear microphone array is proposed to collect acoustical data emitted from the rotation of the shaft. In a semi-anechoic chamber, the proposed technique using single microphone may have detected a crack in propeller shaft by observing the peak of the main spectrum. In engine room on a ship with highly noisy background, we propose microphone co-linear array to increase microphone sensitivity. First, the normal shaft sound was recorded using single sensor, thereafter used as baseline. The cracks were intentionally introduced to the shaft with depth 0.25, 0.5, and 0.75 diameter of the shaft respectively. The sound mixture was separated using frequency domain independent component analysis (TFICA) and then, compared to the accelerometer. The results showed that the measurement accuracy was increased from one sensor to the array and comparable to the direct measurement. These results suggest that the proposed technique may be used as an easy-to-deploy vibration monitoring in a noisy and hazardous engine room.

Time-Frequency Independent Component Analysis for Multi-Damage Detection on a Rotating Machine

Maintenance of the plant machinery plays a crucial factor for maintaining continuity of industrial processes. This paper reported a development of an acoustic-emission-based (A E) technique of identifying multi-damage to the machine remotely using two sensors. In implementation, we emphasized in the separation of sound signals emitted by multiple machines using Time Frequency Independent Component Analysis (TFICA) recorded with a microphone array using the technique of mixing assuming a single source. Overall this study aimed to identify the unbalance, misalignment and bearing faults. Each machine had simultaneously two different damages, i.e. the bearing fault and unbalance, unbalance and misalignment, and bearing fault with misalignment. Separation process was performed using several types of techniques, namely, time domain ICA, frequency domain ICA. Observations FDICA superior in separation rather than TDICA with high MSE values are: 1.7 × 10−5. From the experimental results showed that the distance between the microphone so the shorter the distance the smaller the spatial aliasing occurs.

Frequency-domain-independent vector analysis for mode-division multiplexed transmission

In this paper, we propose a demultiplexing method based on frequency-domain independent vector analysis (FD-IVA) algorithm for mode-division multiplexing (MDM) system. FD-IVA extends frequency-domain independent component analysis (FD-ICA) from unitary variable to multivariate variables, and provides an efficient method to eliminate the permutation ambiguity. In order to verify the performance of FD-IVA algorithm, a 6×6 MDM system is simulated. The simulation results show that the FD-IVA algorithm has basically the same bit-error-rate(BER) performance with the FD-ICA algorithm and frequency-domain least mean squares (FD-LMS) algorithm. Meanwhile, the convergence speed of FD-IVA algorithm is the same as that of FD-ICA. However, compared with the FD-ICA and the FD-LMS, the FD-IVA has an obviously lower computational complexity.

Performance Analysis of Source Image Estimators in Blind Source Separation

Blind methods often separate or identify signals or signal subspaces up to an unknown scaling factor. Sometimes it is necessary to cope with the scaling ambiguity, which can be done through reconstructing signals as they are received by sensors, because scales of the sensor responses (images) have known physical interpretations. In this paper, we analyze two approaches that are widely used for computing the sensor responses, especially, in Frequency-Domain Independent Component Analysis. One approach is the least-squares projection, while the other one assumes a regular mixing matrix and computes its inverse. Both estimators are invariant to the unknown scaling. Although frequently used, their differences were not studied yet. A goal of this work is to fill this gap. The estimators are compared through a theoretical study, perturbation analysis and simulations. We point to the fact that the estimators are equivalent when the separated signal subspaces are orthogonal, and vice versa. Two applications are shown, one of which demonstrates a case where the estimators yield substantially different results.

Convolutive audio source separation using robust ICA and an intelligent evolving permutation ambiguity solution

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem's scalabity with increasing number of sensors. In this paper, the authors propose a multiple-microphone audio source separation algorithm based on a previous work of Mitianoudis and Davies (2003). Complex FastICA is substituted by Robust ICA increasing robustness and performance. Permutation ambiguity is solved using two methodologies. The first is using the Likelihood Ration Jump solution, which is now modified to decrease computational complexity in the case of multiple microphones. The application of the MuSIC algorithm, as a preprocessing step to the previous solution, forms a second methodology with promising results.

A Fast and Efficient Frequency-Domain-Independent Component Analysis for MDM Transmission

In this letter, an improved continuous convergence frequency-domain independent component analysis (ICCFD-ICA) algorithm is proposed for mode-division multiplexed (MDM) transmission, in which a new initialization method for the separation matrix of each frequency bin is proposed to solve the permutation ambiguity. A $6 \,\, \times \,\, 6$ MDM transmission system is simulated to verify the performance of ICCFD-ICA. Simulation results show that the proposed ICCFD-ICA shows a similar performance with the frequency-domain least mean squares (FD-LMS) algorithm and the FD-ICA algorithm. It is also demonstrated that due to the new initialization method for the separation matrices, the convergence speed of the ICCFD-ICA algorithm is improved by 54.5% with respect to FD-ICA; meanwhile, the computational complexity of ICCFD-ICA is lower than both the FD-LMS and the FD-ICA.

주파수영역 독립성분분석을 이용한 수동소나 표적신호 분리

수동소나 시스템에서는 함정의 소음원에서 발생하는 방사 소음을 분석하여 표적을 탐지 및 식별한다. 소나의 탐지 범위 안에 다수의 소음원이 존재하면 신호를 분석할 때 각 소음원에서 나오는 성분들이 혼합되어 각각의 소음원을 규명하기가 어렵다. 이를 해결하기 위해 일반적으로는 배열 센서를 이용한 빔을 형성하여 소음원의 신호를 공간적으로 분리하는 기법이 사용되지만 환경에 따라 여전히 어려운 점이 있다. 본 연구에서는 수동소나 표적신호를 분리하기 위한 새로운 방법으로 주파수영역 독립성분분석(FDICA: Frequency Domain Independent Component Analysis)을 적용하고, 혼합된 표적신호를 분리하는 모의실험을 수행하여 그 타당성을 검증하였다. 표적신호 합성을 위한 특징 정보로는 기계류 토널 성분 및 프로펠러 성분을 사용하였고, 분리 전 후의 결과를 LOFAR(Low Frequency Analysis and Recording), DEMON(Detection Envelope Modulation On Noise) 분석을 통해 비교하였다. Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

Independent vector analysis followed by HMM-based feature enhancement for robust speech recognition

This paper presents a feature-enhancement method that uses the outputs of independent vector analysis (IVA) for robust speech recognition. Although frequency-domain(FD) independent component analysis (ICA) can be successfully used in preprocessing of speech recognition because of its capability of blind source separation (BSS), the performance of the conventional ICA-based approaches is significantly degraded in underdetermined cases. Assuming the target speaker is located relatively close to microphones, the blind spatial subtraction array (BSSA) (Takahashi et al. [10]) tries to enhance target speech features by subtracting noise spectra estimated by FD ICA, even in the underdetermined cases. Unfortunately, the ICA may not be proficient at target speech estimation and then may cause inaccurate noise spectrum estimation. To improve robustness of speech recognition with the inaccurate noise spectra, we introduce Bayesian inference to estimate clean speech features. For a further improvement, FD ICA and delay-and-sum beamforming in the BSSA are replaced with IVA and its target speech output because IVA can improve separation performance without the permutation problem. Experimental results show that the proposed method can further reduce the relative word error rates by 60.11% and 20.07% on average compared to the BSSA for the AURORA2 and DARPA Resource Management databases, respectively.

Using multiple frequency bins for stabilization of FD-ICA algorithms

In the frequency domain independent component analysis approaches for audio sources separation, the convolutive mixing problem is replaced by the solution of several instantaneous mixing problems, one for each frequency bin of the short time Fourier transform. This methodology yields good results but requires the solution of the permutation ambiguity. Moreover, the performance of the separation algorithms for each bin is not guaranteed to be equivalent, thus some bins can have worse results than others. In this paper a technique based on data from multiple bins is proposed to address these issues. The use of multiple bin information produces a coupling of the separation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing the computational cost. This can be mitigated by a sub-sampling of the multiple bins information. The results show that both approaches are beneficial for the frequency domain ICA approach, producing better separation in terms of objective quality measures.

Mode Demultiplexing Based on Frequency-Domain-Independent Component Analysis

Frequency-domain-independent component analysis (F-ICA) algorithm is proposed as demultiplexing method for mode-division multiplexing (MDM) system. We simulate an MDM system and testify the feasibility of F-ICA as demultiplexing algorithm. In addition, the symbol error rate (SER) performance of F-ICA and frequency-domain least mean square algorithm is compared. Simulation results show that the proposed algorithm can implement mode demultiplexing successfully. Meanwhile, it has a better SER performance under the same optical signal-to-noise ratio and coupling factor. We also conduct theoretical analysis of the two algorithms' computational complexity. It is demonstrated that with F-ICA, complexity of DSP is effectively reduced.

High-speed separation of anechoic mixtures of speech signals using fusion of iterative and closed-forms separation method

We introduce the fusion of iterative and closed-forms separation (FICS) method for high-speed separation of anechoic mixed speech signals. This method is performed in two stages: 1) iterative-form separation and 2) closed-form separation. This algorithm significantly improves the separation quality simply due to incorporating only some specific frequency bins into computations. We apply the FICS method to the frequency-domain independent component analysis (ICA) to evaluate its performance in increasing the signal separation speed for anechoic mixtures. Simulation results show that for speech signals and anechoic conditions, the proposed algorithm is on average 65 times faster than ICA while preserving the separation quality. It also outperforms FastICA, JADE, and SOBI in terms of separation quality and speed.

다채널 주파수영역 독립성분분석에서 분리된 신호 전력비의 공분산을 이용한 주파수 빈 정렬

In frequency domain ICA, the frequency bin permutation problem falls off the quality of separated signals. In this paper, we propose a new algorithm to solve the frequency bin permutation problem using the covariance of power ratio of separated signals in multi-channel FD-ICA. It makes use of the continuity of the spectrum of speech signals to check if frequency bin permutation occurs in the separated signal using the power ratio of adjacent frequency bins. Experimental results have shown that the proposed method could fix the frequency bin permutation problem in the multi-channel FD-ICA.

High-speed blind speech separation using FICS method

In this paper, we introduce the fusion of iterative and closed-form separation (FICS) method for high-speed separation of mixed speech signals. This method is performed in two stages: (i) iterative-form separation and (ii) closed-form separation. This algorithm significantly improves the separation quality simply by incorporating only some specific frequency bins into computations. We apply the FICS method to the frequency-domain independent component analysis (ICA) to evaluate its performance in increasing the signal separation speed. Simulation results show that for speech signals, the proposed algorithm is on average 40 times faster than the ICA, while preserving the separation quality. Also, it outperforms the FastICA, JADE, and SOBI in terms of separation quality. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.