Backward Blind Source Separation Research Articles

A novel cascade structure for joint backward blind acoustic noise and echo cancellation systems

This paper addresses joint acoustic echo and punctual noise cancellations framework in hands-free communication systems. A novel cascade structure that combines the backward blind source separation structure (BBSS) as an acoustic noise canceller (ANC), and a conventional acoustic echo canceller (AEC), intended to solve the problem of acoustic echo, is presented. It is proposed to use two adaptive filtering algorithms to update the filter coefficients of each process (ANC and AEC stages). In the first ANC stage, the BBSS structure is combined with the two-channel simplified fast transversal (SFTF) filter algorithm. In the second AEC stage, a single-channel SFTF algorithm is used. Experimental results show both superiority and efficiency of the new structure in comparison with the AEC based SFTF system (without a joint process) in terms of echo return loss enhancement and mean square error gain criteria.

Efficient subband fast adaptive algorithm based-backward blind source separation for speech intelligibility enhancement

This paper addresses the problem of speech intelligibility enhancement by subband adaptive filtering algorithms in a blind framework. Recently in Djendi and Sayoud (Int J Speech Technol 22:391–406, 2019), we have proposed a subband adaptive algorithm based on the forward blind source separation structure that is efficient for acoustic noise reduction and speech intelligibility enhancement applications. In this paper, we propose a novel subband domain implementation of the backward blind source separation structure combined with a modified version of the fast normalized least mean square (FNLMS) algorithm. The new proposed subband algorithm is efficient in improving the speech signal intelligibility without introducing any distortion at the output. A fair comparison of the proposed backward subband FNLMS algorithm with other fullband type algorithms is presented. This comparison is based on the evaluation of several objective criteria. The obtained results show the best performance of the proposed subband algorithm in terms of speed convergence.

A new efficient two-channel fast transversal adaptive filtering algorithm for blind speech enhancement and acoustic noise reduction

This paper addresses the problem of speech enhancement and acoustic noise reduction by blind structures. Recently, the backward blind source separation (BBSS) structure has shown efficiency in cancelling the acoustic noise and improving corrupted speech signals form very noisy observations without any a priori information of source signals. In this paper, we propose a new algorithm based on the combination between the BBSS structure and the simplified fast transversal filter (SFTF) algorithm. The proposed two-channel simplified fast transversal filter (TCSFTF) algorithm succeeded an important blind improvement of steady state and convergence speed performances in diverse noisy situations when only the noisy signals are known. The performances of the new TCSFTF algorithm are compared with four state-of-the-art algorithms in different noisy conditions. This comparison is evaluated in terms of cepstral distance (CD), system mismatch (SM), segmental signal to noise ratio (SegSNR), and segmental mean square error (SegMSE) criteria.

A new efficient backward BSS crosstalk-resistant algorithm for automatic blind speech quality enhancement

In last 10 years, several noise reduction (NR) algorithms have been proposed to be combined with the blind source separation techniques to separate speech and noise signals from blind noisy observations. More often, techniques use voice activity detector (VAD) systems for the optimal solution. In this paper, we propose a new backward blind source separation (BBSS) structure that uses the input correlation properties to provide: (i) high convergence rates and good tracking capabilities, since the acoustic environments imply long and time-variant noise paths, and (ii) low misalignment and robustness against different noise type variations and double-talk. The proposed algorithm has an automatic behavior to enhance noisy speech signals, and do not need any VAD systems to separate speech and noise signals. The obtained results in terms of several objective criteria show the good performance properties of the proposed algorithm in comparison with state-of-the-art algorithms.

Efficient adaptive filtering algorithm for IQ imbalance compensation Tx/Rx systems

This study addresses the problem of in-phase and quadratic-phase (IQ) imbalance in digital transmitter and receiver communication systems. Recently, several adaptive and non-adaptive solutions to this problem have been proposed. In this study, an adaptive solution to this problem is given. The authors propose a new robust two-channel adaptive algorithm to compensate the IQ imbalance problem of quadratic receivers. The new algorithm is based on the combination between the backward blind source separation (BBSS) structure and the fast Newton transversal filter (FNTF) technique to form a two-channel algorithm that cancels the noisy IQ signal adaptively. The combination of the Newton recursion of the FNTF algorithm with the BBSS principle shows the efficiency of the new algorithm to enhance the output signal to interference ratio and allows distortion correction at the output. Simulation results show the efficiency of the new algorithm to compensate the IQ imbalance problem in comparison with the conventional two-channel normalised least mean square algorithm.

An efficient wavelet-based adaptive filtering algorithm for automatic blind speech enhancement

In this paper, we address the problem of speech enhancement by adaptive filtering algorithms. A particular attention has been paid to the backward blind source separation (BBSS) algorithm and its use in crosstalk resistant speech enhancement applications. In this paper, we propose to implement the BBSS algorithm in the wavelet-domain. The proposed backward wavelet BBSS (WBBSS) algorithm is then used in speech enhancement application when important crosstalk interferences are presents. The new WBBSS algorithm shows better performances in terms of convergence speed and steady state in comparison with the classical BBSS one. The performances properties of the proposed algorithm are evaluated in term of segmental SNR (SegSNR), segmental mean square error (SegMSE), and cepstral distance (CD) criteria. The obtained results have confirmed the best performance of the proposed WBBSS algorithm in a lot of situations when blind noisy observations are available.

New automatic forward and backward blind sources separation algorithms for noise reduction and speech enhancement

In this paper, we consider the speech enhancement problem in a moving car through a blind source separation (BSS) scheme involving two spaced microphones. The forward and backward blind source separation structures often use manual voice activity detector (MVAD) systems to control the adaptation of the separating adaptive filters. In this paper, we propose two new automatic voice activity detector (AVAD) systems that allow adapting the original forward and backward BSS structures automatically. The proposed AVAD systems are based on the use of the forward BSS structure to estimate the optimal values of the separating adaptive filters step-sizes. Moreover, the new proposed algorithms are stable and could be used even in very noisy conditions. Intensive experiments are carried out with these two new proposed algorithms to validate their good performances in speech enhancement and noise reduction applications. The presented experiments are based on the system mismatch, the cepstral distance and the output signal-to-noise ratio criteria evaluations. The obtained results show the good performances of the proposed algorithms in comparison with their original versions, where manual VAD systems are used.

A new efficient two-channel backward algorithm for speech intelligibility enhancement: A subband approach

This paper addresses the problem of speech intelligibility enhancement by adaptive filtering algorithms employed with subband techniques. The two structures named the forward and backward blind source separation structures are extensively used in the speech enhancement and source separation areas, and largely studied in the literature with convolutive and non-convolutive mixtures. These two structures use two-microphones to generate the convolutive/non-convolutive mixing signal, and provide at the outputs the target and the jammer signal components. In this paper, we focus our interest on the backward structure employed to enhance the speech signal from a convolutive mixture. Furthermore, we propose a subband implementation of this structure to improve its behavior with speech signal. The new proposed subband-Backward BSS (SBBSS) structure allows a very important improvement of the convergence speed of the adaptive filtering algorithms when the subband-number is selected high. In order to improve the robustness of the proposed subband structure, we have adapted then applied a new criterion that combines the System Mismatch and the Mean-Errors criterion minimization. The proposed subband backward structure, when it is combined with this new criterion minimization, allows to enhance the output speech signal by reducing the distortion and the noise components. The performance of the proposed subband backward structure is validated through several objective criteria which are given and described in this paper.