Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model

To effectively separate underdetermined convolutive mixtures, an accurate and robust underdetermined convolutive blind source separation (BSS) method in the time–frequency domain based on single source points (SSPs) is proposed. Firstly, convolutive BSS in the time domain is transformed into instantaneous BSS in the time–frequency domain by short-time Fourier transform (STFT). Secondly, hierarchical clustering is carried out after the extraction and identification of SSPs by using sparse coding technology. Thirdly, source separation in the frequency domain is obtained based on Lp norm minimization and then alignments are permutated and scales are compensated. Finally, inverse STFT is adopted to reconstruct the separated signals in the time domain. Furthermore, numerical case studies and experimental case studies on a cylindrical test bed are carried out to validate the effectiveness of the proposed method. Generally, this paper provides an accurate and robust underdetermined convolutive BSS technique for mixing matrix estimation, source identification and contribution evaluation, which can provide significant evidence for vibration and noise monitoring and control.

Blind source separation (BSS) in time-frequency (TF) domain is a versatile framework to recover sources from the recorded mixture signals in a reverberant environment. In general, a two-stage strategy is one of the popular BSS frameworks for the underdetermined BSS case (the number of mixtures is less than the number of sources), which is a tough problem due to the mixing matrix is not invertible. In this paper, we propose a new two-stage scheme combining density-based clustering and sparse reconstruction to estimate mixing matrix and sources, respectively. At the first stage, we transform the mixing matrix estimation as an eigenvector clustering problem based on a particular local dominant assumption. The eigenvectors are first exploited from the rank-one structure of local covariance matrices of mixture TF vectors. These eigenvectors are then clustered and adjusted to give estimated mixing matrix by cooperating density-based clustering and weight clustering. At the second stage, we transform the source reconstruction as a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> norm (0 <; p ≤ 1) minimization by an iterative Lagrange multiplier method. With a proper initialization, the obtained solution is a global minimum for any p in (0, 1] with convergence guarantee. The proposed approach is demonstrated to be superior to the state-of-the-art baseline methods in various underdetermined experiments.

Time series modeling of surface EMG based hand manipulation identification via expectation maximization algorithm

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

A novel method is proposed to improve the performance of independent vector analysis (IVA) for blind signal separation of acoustic mixtures. IVA is a frequency-domain approach that successfully resolves the well-known permutation problem by applying a spherical dependency model to all pairs of frequency bins. The dependency model of IVA is equivalent to a single clique in an undirected graph; a clique in graph theory is defined as a subset of vertices in which any pair of vertices is connected by an undirected edge. Therefore, IVA imposes the same amount of statistical dependency on every pair of frequency bins, which may not match the characteristics of real-world signals. The proposed method allows variable amounts of statistical dependencies according to the correlation coefficients observed in real acoustic signals and, hence, enables more accurate modeling of statistical dependencies. A number of cliques constitutes the new dependency graph so that neighboring frequency bins are assigned to the same clique, while distant bins are assigned to different cliques. The permutation ambiguity is resolved by overlapped frequency bins between neighboring cliques. For speech signals, we observed especially strong correlations across neighboring frequency bins and a decrease in these correlations with an increase in the distance between bins. The clique sizes are either fixed, or determined by the reciprocal of the mel-frequency scale to impose a wider dependency on low-frequency components. Experimental results showed improved performances over conventional IVA. The signal-to-interference ratio improved from 15.5 to 18.8 dB on average for seven different source locations. When we varied the clique sizes according to the observed correlations, the stability of the proposed method increased with a large number of cliques.

The issue of convolutive blind source separation (BSS) is addressed in this paper. Independent low-rank matrix analysis (ILRMA), unifying frequency-domain independent component analysis (FDICA) and nonnegative matrix factorization (NMF), is a method that has recently proposed to model low-rank structure of source spectra by using NMF in addition to independence between sources used in FDICA and independent vector analysis (IVA). Although ILRMA has been shown to provide better separation performance than FDICA and IVA, the frequency components of each source are assumed to be independent in ILRMA due to NMF modeling of source spectra, which may degrade its performance when the short-term Fourier transform (STFT) is unable to decorrelate the frequency components for each source. This paper therefore presents a new BSS method that unifies IVA and positive semidefinite tensor factorization (PSDTF). PSDTF models not only power spectra in the same way NMF does but also models the correlations between frequency bins in each source. The proposed method can be viewed as a multichannel extension of PSDTF and exploits both the independence between sources and the inter-frequency correlations as a clue for separating mixtures. Experimental results indicate the improved performance of our approach.

This paper considers the blind separation of the harmonic and percussive components of multichannel music signals. We model the contribution of each source to all mixture channels in the time-frequency domain via a spatial covariance matrix, which encodes its spatial characteristics, and a scalar spectral variance, which represents its spectral structure. We then exploit the spatial continuity and the different spectral continuity structures of harmonic and percussive components as prior information to derive maximum a posteriori (MAP) estimates of the parameters using the expectation-maximization (EM) algorithm. Experimental results over professional musical mixtures show the effectiveness of the proposed approach.

In Recent days Semi supervised image segmentation techniques play a noteworthy role in image processing. Semi supervised image segmentation needs both labeled data and unlabeled data. It means that a Small amount of human assistance or Prior information is given during clustering process. This paper discusses an enhanced semi supervised image segmentation method from labeled image. It uses both a background selection marker and fore ground object selection marker separately. The EM (Expectation Maximization) algorithm is used for clustering along with must link constraints. The proposed method is applied for natural images using MATLAB 7. Thus the proposed method extracts Object of Interest (OOI) from OONI (Object of Not Interest) efficiently and the experimental results are compared with Standard K Means and EM Algorithm also. The results show that the proposed system gives better results than the other two methods. It may also be suitable for object extraction from natural images and medical image analysis.

An image fusion method is proposed based on a kind of estimation theory EM (expectation maximization) algorithm and discrete wavelet frame for merging multiple sensor images. The registered images are firstly decomposed by discrete wavelet frame. Secondly since the fusion of the lowest frequency band is crucial to the final result, they are fused effectively by EM algorithm. The high frequency bands are fused by selection method involving the informative importance measure. Then the fused image is achieved by the discrete wavelet frame invert transform. Experimental results indicate that the proposed method outperforms the discrete wavelet transform and the existing image fusion methods.

This paper considers the blind separation of nonstationary sources in the underdetermined case, when there are more sources than sensors. A general framework for this problem is to work on sources that are sparse in some signal representation domain. Recently, two methods have been proposed with respect to the time-frequency (TF) domain. The first uses quadratic time-frequency distributions (TFDs) and a clustering approach, and the second uses a linear TFD. Both of these methods assume that the sources are disjoint in the TF domain; i.e., there is, at most, one source present at a point in the TF domain. In this paper, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present at a point is strictly less than the number of sensors. The separation can still be achieved due to subspace projection that allows us to identify the sources present and to estimate their corresponding TFD values. In particular, we propose two subspace-based algorithms for TF-nondisjoint sources: one uses quadratic TFDs and the other a linear TFD. Another contribution of this paper is a new estimation procedure for the mixing matrix. Finally, then numerical performance of the proposed methods are provided highlighting their performance gain compared to existing ones

Reverberant signal separation using optimized complex sparse nonnegative tensor deconvolution on spectral covariance matrix

Many parameters can be extracted from a speech signal, including pitch, LPCC, ALPCC, P ARC OR, MFCC, AMFCC, RCEP etc. These parameters have different effectiveness for a speaker recognition system. In order to improve recognition efficiency and obtain a practical speaker recognition system, it is necessary for research feature parameters, that is the main contents of this article. Different parameters are extracted using the method of signal processing including time domain and frequency domain in this paper. These features are analyzed and compared, and the mixed features' effect on the performance of the recognition system is also researched. In order to compare the efficiency of some parameters for speaker recognition system, the identification method based on SVM-VQ on time-frequency domain is chosen. Compared with SVM or VQ recognition method, the method based on SVM-VQ takes less computation, and has better noise immunity and better robustness. The experimental results show that some parameters have great influence on the system performance, such as pitch extracted using wavelet, LPCC and ALPCC, as well as MFCC and AMFCC. The experimental results also show that the recognition rate is obviously improved using mixed parameters in the system.

Within the context of emission tomography, we study volumetric reconstruction methods based on the Expectation Maximization (EM) algorithm. We show, for the first time, the equivalence of the standard implementation of the EM‐based reconstruction with an implementation based on hardware‐accelerated volume rendering for nearest‐neighbor (NN) interpolation. This equivalence suggests that higher‐order kernels should be used with caution and do not necessarily lead to better performance. We also show that the EM algorithm can easily be adapted for different lattices, the body‐centered cubic (BCC) one in particular. For validation purposes, we use the 3D version of the Shepp‐Logan synthetic phantom, for which we derive closed‐form analytical expressions of the projection data. The experimental results show the theoretically‐predicted optimality of NN interpolation in combination with the EM algorithm, for both the noiseless and the noisy case. Moreover, reconstruction on the BCC lattice leads to superior accuracy, more compact data representation, and better noise reduction compared to the Cartesian one. Finally, we show the usefulness of the proposed method for optical projection tomography of a mouse embryo.

Underdetermined mixing matrix estimation by exploiting sparsity of sources

The i-vector representation has become increasingly popular in speaker and language recognition systems. The estimation of the projection matrix of the i-vector model is usually performed using the iterative expectation maximization (EM) algorithm. This work presents a novel approach to estimate the projection matrix of the i-vector representation and to estimate the i-vector representation for each utterance. In this approach, we formulate the estimation of the projection matrix as a principal component analysis (PCA) problem. Using the relation between PCA and a linear Gaussian model trained using the EM algorithm, we show that an approximate solution of the i-vector estimation can be obtained as the solution of a PCA problem. We evaluate the performance of our approximate i-vector estimation on the language recognition task of the robust automatic transcription of speech (RATS) project. The proposed approach reduces by 50% relative the computational time required to estimate the i-vector projection matrix and by 42% relative the computational time to estimate the i-vector representation compared to the standard EM-based approach to i-vector estimation. In addition, our experiments show improvements up to 29% relative in language recognition performance in terms of equal error rate compared to the standard EM-based i-vector estimation.