99 research outputs found
Effective estimation of the desired-signal subspace and its application to robust adaptive beamforming
An effective method is proposed to estimate the desired-signal (S) subspace by the intersection between the signal-plus-interference (SI) subspace and a reference space covering the angular region where the desired signal is located. The estimated S subspace is robust to steering vector mismatch and overestimation of the SI subspace, capable of detecting the relative strength of the desired signal. And even the basis of the estimated S subspace can serve as an effective estimation of the steering vector of the desired signal. With these properties, the estimated S subspace can help to select a more accurate narrow area for searching for the steering vector of the desired signal in mismatch cases. The proposed method is applied for robust adaptive beamforming with an improved performance, as demonstrated by simulation results
Robust Near-Field Adaptive Beamforming with Distance Discrimination
This paper proposes a robust near-field adaptive beamformer for microphone array applications in small rooms. Robustness against location errors is crucial for near-field adaptive beamforming due to the difficulty in estimating near-field signal locations especially the radial distances. A near-field regionally constrained adaptive beamformer is proposed to design a set of linear constraints by filtering on a low rank subspace of the near-field signal over a spatial region and frequency band such that the beamformer response over the designed spatial-temporal region can be accurately controlled by a small number of linear constraint vectors. The proposed constraint design method is a systematic approach which guarantees real arithmetic implementation and direct time domain algorithms for broadband beamforming. It improves the robustness against large errors in distance and directions of arrival, and achieves good distance discrimination simultaneously. We show with a nine-element uniform linear array that the proposed near-field adaptive beamformer is robust against distance errors as large as ±32% of the presumed radial distance and angle errors up to ±20⁰. It can suppress a far field interfering signal with the same angle of incidence as a near-field target by more than 20 dB with no loss of the array gain at the near-field target. The significant distance discrimination of the proposed near-field beamformer also helps to improve the dereverberation gain and reduce the desired signal cancellation in reverberant environments
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology
We propose a new robust distributed linearly constrained beamformer which
utilizes a set of linear equality constraints to reduce the cross power
spectral density matrix to a block-diagonal form. The proposed beamformer has a
convenient objective function for use in arbitrary distributed network
topologies while having identical performance to a centralized implementation.
Moreover, the new optimization problem is robust to relative acoustic transfer
function (RATF) estimation errors and to target activity detection (TAD)
errors. Two variants of the proposed beamformer are presented and evaluated in
the context of multi-microphone speech enhancement in a wireless acoustic
sensor network, and are compared with other state-of-the-art distributed
beamformers in terms of communication costs and robustness to RATF estimation
errors and TAD errors
Signal waveform estimation in the presence of uncertainties about the steering vector
We consider the problem of signal waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed, depending on whether the signal is assumed to be deterministic or random. In the former case, the maximum likelihood (ML) estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations, and an iterative algorithm is presented to obtain the ML weight vector. For random signals, a Bayesian approach is advocated, and we successively derive an (approximate) minimum mean-square error estimator and maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators
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