Post-filtering algorithm for cross-talk cancellation in blind source separation outputs

In this paper, exclusive activity periods (EAPs) are used to model crosstalks in which only one blind source separation (BSS) output signal is assumed to be active and the others are inactive. Inactive intervals of each EAP are used to estimate the crosstalk leaked from each active signal using a least squares method. Then, we use the Wiener filter to cancel the estimated crosstalks from BSS outputs. The benefit of using EAPs is to simplify the estimation of crosstalks from all other signals to the estimation of them from just one active signal in each interval. Thus, it leads to estimate and suppress crosstalks more precisely. A comparison of our method with other popular post-processing algorithms is drawn. The results show an improved performance of the proposed method over earlier approaches.

Convolutive blind source separation and adaptive beamforming have similar goals and similar system structure. Both attempt to extract selected source signals from observed sensor mixtures by a filter array. However, time and frequency information are utilized in convolutive blind source separation while spatial information of source signals or sensor array is used in adaptive beamforming. In this paper, we start with a brief introduction of blind source separation and adaptive beamforming. Next, we review approaches combining spatial information used in beamforming with time/frequency processing used in convolutive blind source separation. We also present a new proposed combination approach and simulation results.

We propose a blind source separation method that yields high-quality speech with low distortion. Time-frequency (TF) masking can effectively reduce interference, but it produces nonlinear distortion. By contrast, linear filtering using a separation matrix such as independent vector analysis (IVA) can avoid nonlinear distortion, but the separation performance is reduced under reverberant conditions. The tandem connectionist approach combines several separation methods and it has been used frequently to compensate for the disadvantages of these methods. In this study, we propose associative memory model (AMM)-based linear filtering and a tandem connectionist framework, which applies TF masking followed by linear filtering. By using AMM trained with speech spectra to optimize the separation matrix, the proposed linear filtering method considers the properties of speech that are not considered explicitly in IVA, such as the harmonic components of spectra. TF masking is applied in the proposed tandem connectionist framework to reduce unwanted components that hinder the optimization of the separation matrix, and it is approximated by using a linear separation matrix to reduce nonlinear distortion. The results obtained in simultaneous speech separation experiments demonstrate that although the proposed linear filtering method can increase the signal-to-distortion ratio (SDR) and signal-to-interference ratio (SIR) compared with IVA, the proposed tandem connectionist framework can obtain greater increases in SDR and SIR, and it reduces the phoneme error rate more than the proposed linear filtering method.

The convolutive blind source separation can be effectively solved in frequency domain, but blind source separation in frequency domain must solve the problem of ranking ambiguity. A frequency-domain blind source separation sorting algorithm is proposed based on regional growth correction. First, the convolutional mixed signal short-time Fourier transform is used to establish an instantaneous model at each frequency point in the frequency domain for independent component analysis. Based on this, the correlation of the power ratio of the separated signal is used to sort all frequency points one by one replacement. Second, according to the threshold, the sorted result is divided into several small areas. Finally. regional replacement and merging is performed according to the regional growth method, and the correct separation signal is finally obtained. Regional growth correction minimizes the mis-proliferation of frequency sorting and improves separation results. The speech blind source separation experiments are performed in the simulated and real environments respectively. The results show the effectiveness of the proposed algorithm.

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.

Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion

Convolutive blind source separation based on joint block Toeplitzation and block-inner diagonalization

Permutation of the outputs at different frequency bins remains as a major problem in convolutive blind source separation (BSS). A coupled hidden Markov model (CHMM) effectively exploits the psychoacoustic characteristics of signals to mitigate such permutations. A joint diagonalization algorithm has been used for convolutive BSS; it incorporates a non-unitary penalty term within the cross-power spectrum-based cost function in the frequency domain. The proposed CHMM system couples a number of conventional HMMs, equivalent to the number of outputs, by making state transitions in each model dependent, not only on its own previous state, but also on some aspects of the state of the other models. Using this method, the permutation effect is substantially reduced; it is demonstrated using a number of simulation studies.

Spiny Lobster Sound Identification Based on Blind Source Separation (BSS) for Passive Acoustic Monitoring (PAM)

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.

Blind Source Separation (BSS) of Mixed Maternal and Fetal Electrocardiogram (ECG) Signal: A comparative Study

This paper describes a novel modification to the well-known natural gradient or INFOMAX algorithm for blind source separation that largely mitigates its divergence problems. The modified algorithm imposes an a posteriori scalar gradient constraint that adds little computational complexity to the algorithm and exhibits fast convergence and excellent performance for fixed step size values that are largely independent of input signal magnitudes and initial separation matrix estimates. Evaluation of the approach for the separation of instantaneous and convolutive source mixtures using both time- and frequency-domain implementations shows its excellent separation behavior.

In this paper, we investigate the importance of the high frequencies in the problem of convolutive blind source separation (BSS) of speech signals. In particular, we focus on frequency domain blind source separation (FD-BSS), and show that when separation is performed in the low frequency bins only, the recovered signals are similar in quality to those extracted when all frequencies are taken into account. The methods are compared through informal listening tests, as well as using an objective measure.

Blind source separation for underdetermined reverberant mixtures is often achieved by assuming a statistical model for cues of interest where the unknown parameters of the statistical model depend on hidden variables. Here, the expectation-maximization (EM) algorithm is employed to compute maximum-likelihood estimates of the unknown model parameters. A by-product of the EM algorithm is a time-frequency (T-F) mask which allows the estimation of the target source from the given mixture. In this paper, we propose the idea of bootstrap averaging to improve separation quality from mixtures recorded under reverberant conditions. Our experiments on real speech mixture signals show an increase in the signal-to-distortion ratio (SDR) over a stateof- the-art baseline algorithm, to our knowledge, currently, the best performing technique in this class of methods.

In optical frequency comb (OFC) undersampling, the downconverted signal spectrum exhibits high spectral density, and traditional single-channel mix signal separation (SC-MSS) algorithms often fail to meet the requirements. To address these challenges, an adaptive genetic mode decomposition (GMD) algorithm is proposed, achieving high-precision adaptive separation of single-channel mixed signals in complex electromagnetic environments. The algorithm initializes the target signal for separation into several genetic modes. The key parameters, such as the number of modes, signal bandwidths, center frequencies, amplitudes, phases, and window functions, are encoded into gene strings (chromosomes). The optimization objectives include minimizing signal reconstruction error, minimizing total bandwidth, and minimizing mode overlap. A genetic population is then constructed to iteratively evolve and optimize the GMD. The simulation results demonstrate that, under a signal-to-noise ratio (SNR) of 0 dB, the proposed GMD algorithm attains an average correlation coefficient of 0.98, an average signal-to-distortion ratio (SDR) of 14.16 dB, and an average signal-to-interference ratio (SIR) of 85.34 dB. Compared to variational mode decomposition (VMD), these metrics represent improvements of 42%, 13.78 dB, and 52.79 dB, respectively, all significantly outperforming the traditional VMD algorithm. Across various signal types, the maximum frequency resolution of the GMD algorithm reaches 100 kHz, with an average resolution enhancement of approximately 80% over VMD. In terms of processing speed, GMD operates at only 11.3% of the runtime required by VMD for the same number of signals.