Klaus Reindl

BSS for improved interference estimation for Blind speech signal Extraction with two microphones

Blind Source Extraction (BSE) as desirable for acoustic cocktail party scenarios requires estimates for the target or interfering signals. Conventional single-channel approaches for obtaining the interference estimate rely on noise and interference estimates during absence of the target signal. For multichannel approaches using multiple microphone signals, a separation of simultaneously active target and interference signals becomes possible if the positions of the target and interfering sources are known. We propose a new method which exploits Directional BSS (Blind Source Separation with a geometric constraint) to estimate the interfering speech sources and diffuse background noise jointly and blindly. Herewith we can effectively deal with the underdetermined BSS scenario (more point sources than sensors) in reverberant environments and can even allow for additional babble noise in the background.

Speech enhancement for binaural hearing aids based on blind source separation

The availability of wireless technologies leads from monaural or bilateral hearing aids to binaural processing strategies. In this paper, we investigate a class of blind source separation (BSS)-based speech enhancement algorithms for binaural hearing aids. The blind binaural processing strategies are analyzed and evaluated for different scenarios, i.e., determined scenarios, where the number of sources does not exceed the number of available sensors and underdetermined scenarios, where there are more active source signals than microphones which is typical for hearing aid applications. These blind algorithms are an attractive alternative to beamforming as no a-priori knowledge on the sensor positions is required. Moreover, BSS algorithms have the advantage that their optimization criteria are solely based on the fundamental assumption of mutual statistical independence of the different source signals.

Geometrically Constrained TRINICON-based relative transfer function estimation in underdetermined scenarios

Speech extraction in a reverberant enclosure using a linearly-constrained minimum variance (LCMV) beamformer usually requires reliable estimates of the relative transfer functions (RTFs) of the desired source to all microphones. In this contribution, a geometrically constrained (GC)-TRINICON concept for RTF estimation is proposed. This approach is applicable in challenging multiple-speaker scenarios and in underdetermined situations, where the number of simultaneously active sources outnumbers the number of available microphone signals. As a most practically relevant and distinctive feature, this concept does not require any voice-activity-based control mechanism. It only requires coarse reference information on the target direction of arrival (DoA). The proposed GC-TRINICON method is compared to a recently proposed subspace method for RTF estimation relying on voice-activity control. Experimental results confirm the effectiveness of GC-TRINICON in realistic conditions.

A two-channel reverberation suppression scheme based on blind signal separation and wiener filtering

In this paper, we apply a blind signal extraction scheme for two microphones to the problem of dereverberation. The system consists of a blocking matrix that cancels the target signal as well as reverberated components up to a certain time lag, thus obtaining a reference not only for noise and interference, but also for late reverberation, which can then be suppressed with a Wiener filter, while leaving early reverberation components largely intact. The performance is assessed in terms of recognition rate of an automatic speech recognizer trained on clean speech, using sentences from the GRID corpus convolved with measured room impulse responses. We show that the system, although primarily developed for noise and interference suppression in low SNR conditions, can significantly suppress reverberation and thereby improve recognition results.

Combination of adaptive feedback cancellation and binaural adaptive filtering in hearing aids

We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, ‘Directional Blind Source Separation (BSS)’ was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated.

Minimum mutual information-based linearly constrained broadband signal extraction

In this contribution, the problem of broadband acoustic signal extraction is treated as a specific source separation problem, where the desired signal components are to be separated from all remaining undesired components. For this, we exploit the generic TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON) framework. The TRINICON optimization criterion is complemented with linear constraints leading to the Linearly Constrained Minimum Mutual Information (LCMMI) criterion for desired signal extraction. A general linearly constrained update rule for iterative filter optimization is derived, which can efficiently be realized in a novel Minimum Mutual Information (MMI)-Generalized Sidelobe Canceler (GSC). The general treatment of the signal extraction problem using an MMI criterion provides several advantages: Firstly, new insights into the signal extraction problem can be derived by establishing links to both the original GSC and the Multichannel Wiener Filter (MWF). Secondly, by exploiting fundamental properties characteristic for speech and audio signals, complicated and often unreliable Voice Activity Detection (VAD)-based control mechanisms become unnecessary. Thirdly, the overall realization requires only prior information of the desired source position. An evaluation of the MMI-GSC for the double-talk situation with two concurrently active speech sources under reverberant and noisy conditions demonstrates the effectiveness of this novel approach.

Linearly-constrained multichannel interference suppression algorithms derived from a minimum mutual information criterion

In this contribution, a generic framework for linearly-constrained multichannel noise and interference suppression algorithms is presented. It is derived from a linearly-constrained minimum mutual information (LCMMI) criterion between mutually statistically independent desired and undesired components, which also accounts for three fundamental signal properties characteristic, e.g., for speech and audio signals: Nonwhiteness, nonstationarity, and nongaus-sianity. We demonstrate links to prominent second order statistics-based algorithms such as the linearly-constrained minimum variance (LCMV) filter and its realization as a generalized sidelobe canceller (GSC). Additionally, we will show how specific supervised constrained and unconstrained multichannel algorithms result as special cases. The presented LCMMI concept leads to new insights for the development of improved adaptation algorithms for noise and interference suppression.

An acoustic front-end for interactive TV incorporating multichannel acoustic echo cancellation and blind signal extraction

In this contribution, an acoustic front-end for distant-talking interfaces as developed within the European Union-funded project DICIT (Distant-talking interfaces for Control of Interactive TV) is presented. It comprises state-of-the-art multichannel acoustic echo cancellation and blind source separation-based signal extraction and only requires two microphone signals. The proposed scheme is analyzed and evaluated for different realistic scenarios when a speech recognizer is used as back-end. The results show that the system significantly outperforms simple alternatives, i.e., a two-channel Delay & Sum beamformer for speech signal extraction.

Informed Spatial Filtering Based on Constrained Independent Component Analysis

In this work, we present a linearly constrained signal extraction algorithm which is based on a Minimum Mutual Information (MMI) criterion that allows to exploit the three fundamental properties of speech and audio signals: Nonstationarity, Nonwhiteness, and Nongaussianity. Hence, the proposed method is very well suited for signal processing of nonstationary nongaussian broadband signals like speech. Furthermore, from the linearly constrained MMI approach, we derive an efficient realization in a (GSC) structure. To estimate the relative transfer functions between the microphones, which are needed for the set of linear constraints, we use an informed time-domain independent component analysis algorithm, which exploits some coarse direction-of-arrival information of the target source. As a decisive advantage, this simplifies the otherwise challenging control mechanism for simultaneous adaptation of the GSC’s blocking matrix und interference and noise canceler coefficients. Finally, we establish relations between the proposed method and other well-known multichannel linear filter approaches for signal extraction based on second-order-statistics, and demonstrate the effectiveness of the proposed signal extraction method in a multispeaker scenario.