Associative Memory Model-Based Linear Filtering and Its Application to Tandem Connectionist Blind Source Separation

The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distribution is adopted as the source prior, which because of its heavy tail nature can better model the large amplitude information in the frequency bins. Therefore it can improve the separation performance and the convergence speed of the IVA and fast version of the IVA (FastIVA) algorithms as compared with the IVA algorithm based on another multivariate super Gaussian source prior. Separation performance with real binaural room impulse responses (BRIRs) is evaluated by detailed simulation studies when using the different source priors, and the experimental results confirm that the IVA and the FastIVA with the proposed multivariate Student's t source prior can consistently achieve improved and faster separation performance.

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.

Independent vector analysis (IVA) is a special form of independent component analysis (ICA), which has demonstrated its prominent performance in solving convolutive blind source separation (BSS) problems in the frequency domain. Most IVA algorithms are based on optimizing certain contrast functions, where the main difficulty of these approaches lies in finding a reliable and fast estimation of the unknown distribution of sources. Despite the rich availability of efficient tensorial approaches to the standard ICA problem, these methods have not been explored considerably for IVA. In this article, we propose a matrix joint diagonalization approach to solve the complex IVA problem. The new factorization neither relies on a whitening process, nor does it require an estimate of the joint probability distribution of the dependent signal groups. The latter is in contrast to most IVA approaches up to date. The underlying geometry of the problem is investigated together with a critical point analysis of the resulting cost function. A conjugate gradient algorithm on the appropriate manifold setting is developed.

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.

A new independent component analysis (ICA) formulation called independent vector analysis (IVA) was proposed in order to solve the permutation problem in convolutive blind source separation (BSS). Instead of running ICA in each frequency bin separately and correcting the disorder with an additional algorithmic scheme afterwards, IVA exploited the dependency among the frequency components of a source and dealt with them as a multivariate source by modeling it with sparse and spherically, or radially, symmetric joint probability density functions (pdfs). In this paper, we compare the speech separation performances of IVA by using a group of l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> -norm-invariant sparse pdfs where the value of and the sparseness can be controlled. Also, we derive an IVA algorithm from a nonparametric perspective with the constraint of spherical symmetry and high dimensionality. Simulation results confirm the efficiency of assuming sparseness and spherical symmetry for the speech model in the frequency domain.

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

A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separation

This study evaluates audio source separation with Fast Independent Component Analysis (FastICA) in a fully specified, reproducible pipeline and benchmarks it against Principal Component Analysis (PCA) and Nonnegative Matrix Factorization (NMF). Three conversational recordings collected at the Department of Statistics, Pabna University of Science and Technology were canonicalized to 48 kHz WAV 96.63 s each, converted to mono, trimmed at 25 dB, RMS‐normalized, and time‐aligned. Sources were mixed with a fixed 3 × 3 row‐normalized Gaussian matrix. FastICA used parallel updates with a logcosh nonlinearity and unit‐variance whitening; PCA served as a multichannel linear baseline; NMF operated on a mono short‐time Fourier transform with KL divergence and NNDSVDA initialization, followed by soft masking and inverse transform. Performance was computed with BSS Eval after best‐permutation and scale alignment and summarized over 10 runs as mean ± SD. FastICA achieved Signal‐to‐Distortion Ratio (SDR) 53.51 ± 0.07 dB, Signal‐to‐Interference Ratio (SIR) 53.52 ± 0.07 dB, and Signal‐to‐Artifact Ratio (SAR) 79.58 ± 0.00 dB, well above a 20 dB high‐quality SDR threshold. PCA yielded SDR 2.79 ± 0.00 dB, SIR 2.79 ± 0.00 dB, SAR 80.64 ± 0.00 dB; NMF produced SDR −2.26 ± 0.00 dB, SIR 0.41 ± 0.00 dB, SAR 4.80 ± 0.00 dB. Waveform and spectrogram visualizations, together with descriptive, high‐order, and entropy statistics, corroborate these outcomes. The results establish FastICA as an effective classical approach for audio source separation and provide a transparent reference pipeline for comparative studies using SDR, SIR, and SAR.

In this study, cross-talk cancellation in convolutive blind source separation (BSS) is investigated using a new post-processing algorithm. In this algorithm, exclusive activity periods (EAPs) are used to model cross-talks in which only one BSS output signal is assumed to be active. Inactive intervals of each EAP are used to estimate the cross-talk leaked from each active signal. Then, the estimated cross-talk is subtracted from BSS outputs. Simulation results show that the proposed algorithm successfully suppresses the crosstalk by improving the signal-to-interference ratio (SIR) and signal-to-distortion ratio (SDR) of BSS outputs about 12 and 5 dB, respectively.

The distinction of the lead varying vocals from the background music in an audio recording is an extremely demanding and exigent task. The speech-separation research usually inculcates Time-frequency masking technique that ultimately appraises the hearing-aid design. The core principle in music which is capitalized to discriminate underlying non-vocals from vocals (speech) is Repetition. The `Repetition' feature is especially enacted for pop songs where the singer often overlays frequently changing vocals on a periodically repeating background in a mixture. The basic approach of this research paper is the recognisation of periodically repeating segments in audio excerpts, analogize them with a repeating model and finally discrimate the repeating musical patterns via Time-Frequency masking. A TF mask is grounded on the basis of TF representation of any signal. In the proposed algorithm, the quality of foreground vocals and accompanying background can be adjudicated in terms of SIR (Signal to Interference Ratio) value utilizing `ANOVA' (Analysis Of Variation) computational method on six different genres of musical audios and formulated the complete comparison of SIR values using hamming, hanning and blackmann windows and concluded that separation of mono-aural vocal and non-vocal components applying blackmann window shows better SIR values as compared to hanning and hamming windows.

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments.

Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove. Independent component analysis (ICA) is a well-known technique of blind source separation (BSS) and is extensively used in literature for ECG artifact elimination. In this article, the independent vector analysis (IVA) is used for artifact removal in the ECG data. This technique takes advantage of both the canonical correlation analysis (CCA) and the ICA due to the utilization of second-order and high order statistics for un-mixing of the recorded mixed data. The utilization of recorded signals along with their delayed versions makes the IVA-based technique more practical. The proposed technique is evaluated on real and simulated ECG signals and it shows that the proposed technique outperforms the CCA and ICA because it removes the artifacts while altering the ECG signals minimally.

Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many real-time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone to separate speech and noise in real-world scenarios for hearing-aid applications. The experimental results with objective and subjective tests reveal the usefulness of the developed method for real-world applications.

In this paper, we address the problem of underdetermined blind source separation (BSS) of anechoic speech mixtures. We propose a demixing algorithm that exploits the sparsity of certain time-frequency expansions of speech signals. Our algorithm merges lscrq -basis-pursuit with ideas based on the degenerate unmixing estimation technique (DUET) [Yiotalmaz and Rickard, Blind Source Separation of Speech Mixtures via Time-Frequency Masking, IEEE Transactions on Signal Processing, vol. 52, no. 7, pp. 1830-1847, July 2004]. There are two main novel components to our approach: 1, our algorithm makes use of all available mixtures in the anechoic scenario where both attenuations and arrival delays between sensors are considered, without imposing any structure on the microphone positions, and 2, we illustrate experimentally that the separation performance is improved when one uses lscrq-basis-pursuit with q < 1 compared to the q = 1 case. Moreover, we provide a probabilistic interpretation of the proposed algorithm that explains why a choice of 0.1 les q les 0.4 is appropriate in the case of speech. Experimental results on both simulated and real data demonstrate significant gains in separation performance when compared to other state-of-the-art BSS algorithms reported in the literature.

Fast fixed-point independent vector analysis algorithms for convolutive blind source separation