In this paper we focus on Bayesian blind and semi-blind adaptive signal processing based on a broadband MIMO FIR model (e.g., for blind source separation (BSS) and blind system identification (BSI)). Specifically, we study in this paper a framework allowing us to systematically incorporate various types of prior knowledge: (1) source signal statistics, (2) deterministic knowledge on the mixing system, and (3) stochastic knowledge on the mixing system. In order to exploit all possible types of source signal statistics (1), our considerations are based on TRINICON, a previously introduced generic framework for broadband blind (and semi-blind) adaptive MIMO signal processing. The motivation for this paper is threefold: (a) the extension of TRINICON to Bayesian point estimation to address (3) in addition to (1), and (b) more specifically to unify system-based blind adaptive MIMO signal processing with the tracking of time-varying scenarios, and finally (c) to show how the Bayesian TRINICON-based tracking can be formulated as a sequence estimation approach on arbitrary partly smooth manifolds. As we will see in this paper, the Bayesian approach to incorporate stochastic priors and the manifold learning approach to exploit deterministic system knowledge (2) complement one another very efficiently in the context of TRINICON.

In this paper we introduce a novel class of efficient multichannel adaptive filtering algorithms for sparse FIR systems. By suitably integrating ideas from compressed sensing and adaptive filter theory, this class of algorithms allows to significantly reduce the actual number of adaptive coefficients in an efficient way. These algorithms, termed compressive-domain adaptive filters, can be interpreted as a novel type of transform-domain techniques. They can also be seen as adaptive approach in an efficiently self-learning manifold based on the prior knowledge of sparseness of the system. An important property of this concept is that it does not place additional restrictions on the input signal characteristics. Based on the well-known RLS algorithm as a reference, the simulation results confirm that the proposed algorithm converges at acceptable rates, even for strongly colored signals such as speech and audio.

In many teleconferencing applications using modern laptop and net-book devices it is common to encounter annoying keyboard typing noise. In this paper we propose an acoustic keystroke transient canceler for speech communication terminals as a novel broadband adaptive filter application in such a hands-free scenario. We present this approach in the context of the Google Chromebook Pixel device which is equipped with a special audio reference channel providing various new signal processing possibilities. Our novel semi-blind/semi-supervised approach exploiting this new degree of freedom, combined with the system-based broadband estimation and a novel adaptation control yields a high-quality speech enhancement even under challenging acoustic conditions.

In this paper a problem in transient noise suppression for audio streams in laptop and netbook devices is addressed. One or more microphones record voice signals which are corrupted with ambient noise and also transient noise from keyboard and mouse clicks. In the current work, a synchronous reference microphone is embedded in the keyboard which allows for measurement of the key click noise, substantially unaffected by the voice signal and ambient noise. An algorithm is here presented for incorporation of the keybed microphone as a reference signal in a signal restoration process for the voice part. The problem is substantially complicated by the presence of nonlinear vibarations (we postulate) in the hinge and casework of the laptop, which renders a simple linear suppressor ineffective in some cases. Moreover, the transfer functions between key clicks and voice microphone depend strongly upon which key is being clicked. A very low-latency solution is proposed in which short-time transform data is processed sequentially in short frames and a robust statistical model is formulated and estimated using Bayesian inference procedures. Results with real recordings show a significant reduction of typing artefacts at the expense of small amounts of voice distortion.

Recently developed multichannel adaptive filtering algorithms aim at spatio-temporal decoupling of the signals by suitably chosen transformations. In this paper we establish the relation between the techniques of transform-domain adaptive filtering with application to multichannel acoustic cancellation. The link between the recently introduced source-domain and eigenspace adaptive filtering algorithms is shown by means of a generic spatial transform-domain adaptive filtering algorithm. We discuss the difference between regularizing the identification problem in the source domain and in the system eigenspace. Further, we study the estimation of the multiple-input multiple-output (MIMO) system eigenspace without modifying the highly cross correlated input signals or requiring prior knowledge of the system and highlight the validity of the estimated eigenspace due to system changes. Finally, we give simulation results proving our concept.

Acoustic echo suppression (AES) provides an attractive alternative to acoustic echo cancellation (AEC) techniques for full-duplex communication in low-complexity systems. However, so far AES techniques are commonly known to introduce significant distortions to the desired signal. Moreover, most traditional echo control techniques typically require accurately detecting the contribution of the near-end speaker to the microphone signal (“double talk”). The extension of AES techniques to the multichannel case usually assumes a symmetric system design which is often not fulfilled by typical scenarios. In this paper we propose a novel approach to multichannel acoustic echo suppression, which aims at extracting the near-end signal using a constraint for a distortionless output, without requiring a double-talk detector, or a symmetric system design. In addition to the above mentioned properties, the multichannel AES is also shown to overcome the known challenges in conventional multichannel acoustic echo control setups.

This paper studies an echo suppression approach to reducing the undesired echoes that result from the acoustic coupling between a loudspeaker and a microphone in duplex voice communication. The approach consists of four basic steps. First, both the loudspeaker and microphone signals are partitioned into small overlapping frames. Second, each frame is transformed into the short-time Fourier transform (STFT) domain. Third, a minimum variance distortionless response (MVDR) filter is designed in each subband by explicitly using the interframe signal correlation. This MVDR filter is then used to estimate the echo signal and the obtained estimate is subsequently subtracted from the microphone signal. Finally, the time-domain processed signal is constructed using the overlap-add technique with the inverse STFT. Experiments are performed and the results demonstrate that this proposed method can achieve significant amount of echo suppression in practical room environments.

Acoustic echo suppression techniques for full-duplex communication in low-complexity systems are commonly known to introduce distortion to the desired signal (i.e., near-end speech). Moreover, most traditional echo control techniques typically require accurately detecting the contribution of the near-end speaker to the microphone signal (“double talk”). In this letter, we propose a novel approach to acoustic echo suppression, which aims at extracting the near-end signal using a constraint for minimizing the distortion, and without requiring a double-talk detector.

Hands-free full-duplex communication systems require acoustic echo cancelers to reduce echoes. Multichannel sound reproduction enhances realism in virtual reality and multimedia communication systems. However, in the case of multichannel systems the acoustic echo cancellation problem is challenging because of the non-uniqueness of the solution in the least squares sense. There fore, preprocessing techniques are required. Known preprocessing approaches act in the temporal domain. In this paper we propose an acoustic echo cancellation preprocessing stage which bases on the spatial diversity offered by massive multichannel reproduction systems.

In this paper, we show that minimization of the statistical dependence using broadband independent component analysis (ICA) can be successfully exploited for acoustic source localization. As the ICA signal model inherently accounts for the presence of several sources and multiple sound propagation paths, the ICA criterion offers a theoretically more rigorous framework than conventional techniques based on an idealized single-path and single-source signal model. This leads to algorithms which outperform other localization methods, especially in the presence of multiple simultaneously active sound sources and under adverse conditions, notably in reverberant environments. Three methods are investigated to extract the time difference of arrival (TDOA) information contained in the filters of a two-channel broadband ICA scheme. While for the first, the blind system identification (BSI) approach, the number of sources should be restricted to the number of sensors, the other methods, the averaged directivity pattern (ADP) and composite mapped filter (CMF) approaches can be used even when the number of sources exceeds the number of sensors. To allow fast tracking of moving sources, the ICA algorithm operates in block-wise batch mode, with a proportionate weighting of the natural gradient to speed up the convergence of the algorithm. The TDOA estimation accuracy of the proposed schemes is assessed in highly noisy and reverberant environments for two, three, and four stationary noise sources with speech-weighted spectral envelopes as well as for moving real speech sources.

Combining the channels of a multiple input/multiple output (MIMO) system into suitably chosen modes by a domain transformation offers great improvements of adaptive filtering algorithms. In this paper we present an algorithm for adaptive MIMO filtering, called source-domain adaptive filtering (SDAF), with application to multichannel acoustic echo cancellation operating in an optimally adjusted transform domain without requiring a-priori knowledge about the system. Experimental results show a significant performance improvement compared to fixed transformation bases.

In this paper, we propose a versatile acoustic source localization framework exploiting the self-steering capability of Blind Source Separation (BSS) algorithms. We provide a way to produce an acoustical map of the scene by computing the averaged directivity pattern of BSS demixing systems. Since BSS explicitly accounts for multiple sources in its signal propagation model, several simultaneously active sound sources can be located using this method. Moreover, the framework is suitable to any microphone array geometry, which allows application for multiple dimensions, in the near field as well as in the far field. Experiments demonstrate the efficiency of the proposed scheme in a reverberant environment for the localization of speech sources.

In this paper we propose two novel methods for preserving the spatial information in source separation algorithms. Our approach is applicable to any source separation algorithm and is based on an additional supervised adaptive filtering with the reference signals generated by the source separation system. If a special constrained optimization scheme is applied to derive the source separation algorithm then the novel approach can be simplified. The quality of the spatial representation and the separation performance of both methods and two state-of-the-art approaches from the literature have been evaluated by a MUSHRA listening test according to the relevant ITU recommendation showing that the novel methods clearly outperform the state-of-the-art approaches.

Acoustic echo cancellation (AEC) has become an essential and well-known enabling technology for hands-free communication and human-machine interfaces. AEC for two or more reproduction channels aims at identifying the echo paths between the microphone and each audio reproduction source in order to cancel the associated echo contribution. A number of preprocessing methods have been proposed to decorrelate stereo audio signals in order to enable an unambiguous identification of each echo path and to thus ensure robustness to changing sound source locations. While several of these methods provide enough decorrelation to achieve proper AEC convergence in the stereo case, considerations of subjective sound quality have frequently not been addressed adequately. This paper compares the performance of several methods in terms of both convergence speed and aspects of sound perception, and proposes a novel signal decorrelation approach with attractive properties. The superior performance of the proposed method is demonstrated for 5.1 surround sound reproduction.

This paper addresses the TDOA extraction problem for localizing multiple sources in noisy and reverberant environments with emphasis on speech excitation. TDOAs are estimated by performing blind adaptive MIMO system identification using a gradient-based BSS variant of the TRINICON framework. We present a novel method to improve the TDOA estimation for signals with lowpass-like spectral characteristics such as speech, for which the standard approach achieves only imperfect system identification. To this end, we propose to combine the BSS algorithm with decorrelation filters, thereby achieving a greatly improved wideband identification of the acoustical system. The approach is verified in a number of scenarios, where it provides more accurate TDOA estimates for the speaker localization at a negligible additional computational cost.

Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust M-estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference

Convolutive blind source separation (BSS) aims at separating point sources from mixtures picked up by several sensors. In real-world environments moving speakers, background noise and long reverberation are encountered which often degrade the performance of BSS algorithms. In such cases, the application of a post-filter can improve the output signal quality by suppression of residual cross-talk and of background noise. In this paper we propose a novel technique to estimate the necessary power spectral densities of the cross-talk components and present a robust system which allows to further suppress both, the remaining interference from point sources and the background noise. Experimental results show the benefit of this post-processing method in realistic environments

Blind source separation (BSS) algorithms are often categorized as either narrowband or broadband algorithms depending on whether their respective cost functions aim at individual DFT bins or the entire broadband signal. In this contribution, we present comparable general natural gradient-based formulations of both concepts based on the TRINICON framework. As a distinctive feature, narrowband algorithms imply an internal permutation and scaling problem. We show that the common DOA estimation-based methods for aligning the permutations effectively rely on geometric a-priori knowledge, and we explain why they need to be complemented by additional repair mechanisms for robust BSS. The latter can already be viewed as approximations of the generic TRINICON broadband algorithm. As a conclusion, we propose to always use a generic broadband algorithm as a starting point for the design of new BSS algorithms

We propose an integrated acoustic echo cancellation solution based on a novel class of efficient and robust adaptive algorithms in the frequency domain, the extended multidelay filter (EMDF). The approach is tailored to very long adaptive filters and highly auto-correlated input signals as they arise in wideband full-duplex audio applications. The EMDF algorithm allows an attractive tradeoff between the well-known multidelay filter and the recursive least-squares algorithm. It exhibits fast convergence, superior tracking capabilities of the signal statistics, and very low delay. The low computational complexity of the conventional frequency-domain adaptive algorithms can be maintained thanks to efficient fast realizations. We also show how this approach can be combined efficiently with a suitable double-talk detector (DTD). We consider a corresponding extension of a recently proposed DTD based on a normalized cross-correlation vector whose performance was shown to be superior compared to other DTDs based on the cross-correlation coefficient. Since the resulting DTD also has an EMDF structure it is easy to implement, and the fast realization also carries over to the DTD scheme. Moreover, as the robustness issue during double talk is particularly crucial for fast-converging algorithms, we apply the concept of robust statistics into our extended frequency-domain approach. Due to the robust generalization of the cost function leading to a so-called M-estimator, the algorithms become inherently less sensitive to outliers, i.e., short bursts that may be caused by inevitable detection failures of a DTD. The proposed structure is also well suited for an efficient generalization to the multichannel case

Using a recently presented generic framework for multichannel blind signal processing for convolutive mixtures, we investigate the problem of incorporating acausal delays which are necessary with certain geometric constellations. Starting from a generic update equation which is applicable to blind source separation (BSS), multichannel blind deconvolution (MCBD), and multichannel blind partial deconvolution (MCBPD) for dereverberation of speech signals, two formulations of the natural gradient are derived. It is shown that one expression is applicable to mere causal filters whereas the other also allows an implementation of noncausal filters. Moreover, proper initialization methods for both cases are given. For the implementation of these algorithms, cross-relation estimation techniques, known from linear prediction, are discussed. Based on these results, relationships between traditional MCBD algorithms can be established. Experimental results of different acoustic scenarios show the applicability of the presented algorithms.

Blind adaptive filtering for time delay of arrival (TDOA) estimation is a very powerful method for acoustic source localization in reverberant environments with broadband signals like speech. Based on a recently presented generic framework for blind signal processing for convolutive mixtures, called TRINICON, we present a TDOA estimation method for simultaneous multidimensional localization of multiple sources. Moreover, an interesting link to the known single-input multiple-output (SIMO)-based adaptive eigenvalue decomposition (AED) method is shown. We evaluate the novel multiple-input multiple-output (MIMO)-based approach and compare it with the known SIMO-based method in a reverberant acoustic environment using reference data of the positions obtained from infrared sensors. The results show that the new approach is very robust against reverberation and background noise.

We present a general broadband approach to blind source separation (BSS) for convolutive mixtures based on second-order statistics. This avoids several known limitations of the conventional narrowband approximation, such as the internal permutation problem. In contrast to traditional narrowband approaches, the new framework simultaneously exploits the nonwhiteness property and nonstationarity property of the source signals. Using a novel matrix formulation, we rigorously derive the corresponding time-domain and frequency-domain broadband algorithms by generalizing a known cost-function which inherently allows joint optimization for several time-lags of the correlations. Based on the broadband approach time-domain, constraints are obtained which provide a deeper understanding of the internal permutation problem in traditional narrowband frequency-domain BSS. For both the time-domain and the frequency-domain versions, we discuss links to well-known, and also, to novel algorithms that constitute special cases. Moreover, using the so-called generalized coherence, links between the time-domain and the frequency-domain algorithms can be established, showing that our cost function leads to an update equation with an inherent normalization ensuring a robust adaptation behavior. The concept is applicable to offline, online, and block-online algorithms by introducing a general weighting function allowing for tracking of time-varying real acoustic environments.

In this paper we present a framework for multichannel blind signal processing for convolutive mixtures, such as blind source separation (BSS) and multichannel blind deconvolution (MCBD). It is based on the use of multivariate pdf and a compact matrix notation which considerably simplifies the representation and handling of the algorithms. By introducing these techniques into an information theoretic cost function, we can exploit the three fundamental signal properties nonwhiteness, nongaussianity, and nonstationarity. This results in a versatile tool that we call TRINICON (Triple-N ICA for convolutive mixtures). Both, links to popular algorithms and several novel algorithms follow from the general approach. In particular, we introduce a new concept of multichannel blind partial deconvolution (MCBPD) for speech which prevents a complete whitening of the output signals, i.e., the vocal tract is excluded from the equalization. This is especially interesting for automatic speech recognition applications. Moreover, we show results for BSS using multivariate spherically invariant random processes (SIRP) to efficiently model speech, and show how the approach carries over to MCBPD. These concepts are also suitable for an efficient implementation in the frequency domain by using a rigorous broadband derivation avoiding the internal permutation problem and circularity effects.

For high-quality multimedia communication systems such as teleconferencing or virtual reality applications, multichannel sound reproduction is highly desirable. While progress has been made in stereo and multichannel acoustic echo cancellation (MC AEC) in recent years, the corresponding sound reproduction systems still imply a restrained listening area ('sweet spot'). A volume solution for spatial sound in a large listening area is offered by wave field synthesis (WFS) or by ambisonics, where arrays of loudspeakers generate a prespecified sound field. However, before this new technique can be utilized for full-duplex systems, an efficient solution to the MC AEC problem has to be found. This paper presents a novel approach that extends the current state of the art of MC AEC and transform-domain adaptive filtering by reconciling the flexibility of adaptive filtering and the underlying physics of acoustic waves in a systematic and efficient way. To achieve this, the new framework of wave-domain adaptive filtering (WDAF) exploits the spatial information provided by densely sampled contours for both recording and reproduction. Experimental results with a 32-channel AEC verify the concept for both simulated and actually measured room acoustics.

Wave field synthesis is an auralization technique which allows the entire wave field to be controlled within the entire listening area. However, reflections in the listening room interfere with the auralized wave field and may impair the spatial reproduction. Active listening room compensation aims at reducing these impairments by using the playback system. Due to the high number of playback channels used for wave field synthesis, the existing approaches to room compensation are not applicable. A novel approach to active room compensation overcomes these problems by a transformation from the space-time to the wave domain and application of wave-domain adaptive filtering.

We propose a novel class of efficient adaptive algorithms in the frequency domain that is tailored to very long adaptive filters and highly autocorrelated input signals as they arise, e.g., in high-quality full-duplex audio applications. The approach exhibits good tracking capabilities of the signal statistics and very low delay. Moreover, it is shown that the low order of computational complexity of the conventional frequency-domain adaptive algorithms can be maintained thanks to efficient realizations. The algorithm allows a tradeoff between the well-known multidelay filter (MDF) and the recursive least-squares (RLS) algorithm. It is also well suited for an efficient generalization to the multichannel case.

In this combustion, we embed full-duplex multichannel communication interfaces for tele-presence systems into a general framework. On the reproduction side, we consider a wide range of multichannel acoustic rendering techniques including traditional stereophony, '5.1' systems, and wave field synthesis using loud speaker arrays for sound immersion. On the recording side, microphone arrays are discussed for capturing clean desired signals with spatial information. Based on this general framework, real-time implementations of such full-duplex multichannel communication systems are then described. We combine wave field synthesis with multichannel acoustic echo cancellation and adaptive beamforming and discuss a real-time implementation on standard desktop and laptop PCs.

For multichannel adaptive filtering applications like acoustic echo cancellation, sophisticated, but efficient algorithms are necessary that take both auto- and cross-correlations of the excitation signals into account. Based on a recently published frequency-domain framework, we examine in this contribution the computation of the so-called frequency-domain Kalman gain, which is crucial in the multichannel case to meet the above requirements for correlated input signals. We present an extended scheme with increased computational efficiency for a higher number of input channels. Robust convergence behaviour during perturbation of the desired signal is ensured by a new dynamical regularization. Application of the new algorithm to multichannel acoustic echo cancellation with real-world signals achieves improved performance.

For high-quality multimedia communication systems, such as teleconferencing, or tele-teaching (especially of music), multichannel sound reproduction is highly desirable. While current approaches still rely on a restrained listening area, the sweet spot, a volume solution for a large listening space is offered by the wave field synthesis (WFS) method, where arrays of loudspeakers generate a prespecified sound field. On the recording side of the two-way systems, the use of microphone arrays is an effective approach to cope with undesired signal components in the receiving room. However, before full-duplex communication can be deployed, efficient approaches to the acoustic echo cancellation (AEC) problem in this challenging scenario have to be found. We investigate different options for system integration, after a brief discussion of the current state of the art. We then present a first real-time solution on a regular PC platform, based on an efficient AEC for MIMO (multi-input and multi-output) systems in the frequency-domain.

For hands-free acoustical human/machine interfaces, e. g. for automatic speech recognition or teleconferencing systems, microphone arrays using robust Generalized Sidelobe Cancellers (GSCs) in conjunction with acoustic echo cancellation (AEC) can be efficiently applied for optimum communication. This contribution devises a new structure for combining AEC and GSC. It reduces the computational complexity by more than a factor of ten relative to a time-domain arrangement, increases convergence speed, and preserves positive synergies. 1.

For hands-free man-machine audio interfaces with multichannel sound reproduction and automatic speech recognition (ASR), sometimes both an acoustic echo canceller (AEC) and a beamforming (BF) microphone array are necessary for sufficient recognition rates. In the context of multimedia systems, multi-channel sound reproduction (e.g. stereo or 5.1 channel-surround systems) typically requires multi-channel AEC (M-C AEC). With M-C AEC being known to be a very demanding problem in signal processing, no practically relevant simulation results have yet been presented for more than two channels. We examine a previously proposed frequency-domain adaptive filtering scheme with some extensions for the case of more than two loudspeaker channels. The results using this approach show a remarkable performance at a relatively moderate computational complexity. Moreover, we show how to efficiently combine this M-C AEC structure with microphone arrays.