Automatic detection of drowsiness in EEG records based on multimodal analysis.

Real-time and reliable location information of mobile nodes is a key enabler for many emerging wireless network applications. Such information can be obtained via network navigation, a new paradigm in which nodes exploit both spatial and temporal cooperation to infer their positions. In this paper, we establish a theoretical foundation for network navigation and determine the fundamental limits of navigation accuracy using equivalent Fisher information analysis. We then introduce the notion of carry-over information and provide a geometrical interpretation for the evolution of navigation information. Our framework unifies the navigation information obtained from spatial and temporal cooperation, leading to a deep understanding of information evolution and cooperation benefits in navigation networks.

Network Navigation: Theory and Interpretation

In this paper, we consider the joint optimization of the source power allocation and relay beamforming weights in distributed multiuser peer-to-peer (MUP2P) relay networks applying the amplify-and-forward (AF) protocol. We adopt a quality-of-service (QoS) based approach, in which the total power transmitted from all sources and relays is minimized while guaranteeing the prescribed QoS requirement of each source-destination pair. The QoS is modeled as a function of the receive signal-to-interference-plus-noise ratio (SINR) at the destinations. Unlike the existing contributions, the transmitted powers of the sources and the beamforming weights of the relays are optimized jointly in this paper. Introducing an appropriate transformation of variables, the QoS based source power allocation and distributed relay beamforming (PADB) problem can be equivalently transformed into a difference of convex (DC) program, which can be efficiently solved with local optimality using the constrained concave convex procedure (CCCP). Based on this procedure, we also propose an iterative feasibility search algorithm (IFSA) to find an initial feasible point of the DC program. The analytic study of the proposed solution confirms that it converges to a local optimum of the PADB problem. Numerical results show that our solution outperforms (in terms of the total transmitted power) the alternating optimization procedure and the exact penalty based DC algorithm. In addition, the proposed IFSA outperforms the alternating optimization algorithm in finding feasible points of the DC program (i.e., the equivalence of the PADB problem).

Joint Optimization of Source Power Allocation and Distributed Relay Beamforming in Multiuser Peer-to-Peer Relay Networks

In this chapter, we explore many of the basic concepts of array processing with an emphasis on adaptive beamforming for speech enhancement applications. We begin in Sect. 47.1 by formulating the problem of microphone array in a noisy and reverberant environment. In Sect. 47.2, we derive the frequency-domain linearly constrained minimum-variance (LCMV) beamformer, and its generalized sidelobe canceller (GSC) variant. The GSC components are explored in Sect. 47.3, and several commonly used special cases of these blocks are presented. As the GSC structure necessitates an estimation of the speech related acoustical transfer functions (ATFs), several alternative system identification methods are addressed in Sect. 47.4. Beamformers often suffer from sensitivity to signal mismatch. We analyze this phenomenon in Sect. 47.5 and explore several cures to this problem. Although the GSC beamformer yields a significant improvement in speech quality, when the noise field is spatially incoherent or diffuse, the noise reduction is insufficient and additional postfiltering is normally required. In Sect. 47.6, we present multi-microphone postfilters, based on either minimum mean-squared error (MMSE) or log-spectral amplitude estimate criteria. An interesting relation between the GSC and the Wiener filter is derived in this Section as well. In Sect. 47.7, we analyze the performance of the transfer-function GSC (TF-GSC), and in Sect. 47.8 demonstrate the advantage of multichannel postfiltering over single-channel postfiltering in nonstationary noise conditions.

Adaptive Beamforming and Postfiltering

Real-time matrix inversion is a key enabling technology in multiple-input multiple-output (MIMO) communications systems, such as 802.11n. To date, however, no matrix inversion implementation has been devised which supports real-time operation for these standards. In this paper, we overcome this barrier by presenting a novel matrix inversion algorithm which is ideally suited to high performance floating-point implementation. We show how the resulting architecture offers fundamentally higher performance than currently published matrix inversion approaches and we use it to create the first reported architecture capable of supporting real-time 802.11n operation. Specifically, we present a matrix inversion approach based on modified squared Givens rotations (MSGR). This is a new QR decomposition algorithm which overcomes critical limitations in other QR algorithms that prohibits their application to MIMO systems. In addition, we present a novel modification that further reduces the complexity of MSGR by almost 20%. This enables real-time implementation with negligible reduction in the accuracy of the inversion operation, or the BER of a MIMO receiver based on this.

QR Decomposition-Based Matrix Inversion for High Performance Embedded MIMO Receivers

Deleted DOI: Postfiltering Using Multichannel Spectral Estimation in Multispeaker Environments

This paper presents a new space constrained adaptive beamformer employing an updated source power spectral density (PSD). The space constraints are used to capture the target signal spatially and to provide robustness against steering error vectors. The PSD update on the other hand ensures that the spectral information of the desired source is reflected continuously on the space constraints. As such, target signal extraction can be achieved with minimum distortion. The beamformer operates in a subband structure to allow time-frequency operation for each channel, yielding a combination of weighted spatial and temporal filters. Evaluations on real car data show that the proposed algorithm significantly improves the speech intelligibility with noise suppression level up to 21 dB.

Space constrained beamforming with source PSD updates

In this paper, we present a new subband adaptive beamformer equipped with noise statistics updates. These updates are employed to effectively estimate and track the noise statistics continuously in the solution. Additionally, an update on the source power spectral density (PSD) is incorporated to enhance the timbre of the source of interest. Furthermore, the beamformer is also equipped with a space constraint on the source area to provide robustness against steering vector errors and good capture of the target signal spatially. The entire processing can be viewed as an efficient combination of weighted spatial and temporal filters. Evaluations on real car data with variations in the car noise level show that the proposed scheme achieves a good noise suppression level up to 20 dB.

Adaptive microphone array with noise statistics updates

This paper presents a new blind speech separation algorithm using beamforming technique that is capable of extracting each individual speech signal from a mixture of three speech sources in a room. The speech separation algorithm utilizes the steered response power phase transform for obtaining a localization estimate for each individual speech source in the frequency domain. Based on those estimates each desired speech signal is extracted from the speech mixture using an optimal beamforming technique. To solve the permutation problem, a permutation alignment algorithm based on the mutual output correlation is employed to group the output signals into the correct sources from each frequency bin. Evaluations using real speech recordings in a room environment show that the proposed blind speech separation algorithm offers high interference suppression level whilst maintaining low distortion level for each desired signal.

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Source separation employing beamforming and SRP-PHAT localization in three-speaker room environments

The convenience and the ease of use provided by hands-free operation of speech communication devices mean that speech enhancement schemes are becoming indispensable. In this chapter, two subband adaptive microphone array schemes are presented, which aim to provide good speech enhancement capability in poor signal to noise ratio situations. The basic commonality of the adaptive microphone array schemes is that they approximate the Wiener solution in an adaptive manner as new data comes in. Furthermore, both schemes include a quadratic constraint to prevent the trivial zero solution of the weights and to avoid suppression of the source of interest. The constraint is included to provide robustness against model mismatch and good spatial capture of the target signal. Furthermore, by using a subband structure the processing allows a time-frequency operation for each channel. As such, both schemes utilize the spatial, spectral, and temporal domains in an efficient and concise manner allowing a computational effective processing while maintaining high performance speech enhancement. Evaluations on the same data set, gathered from a car, show that the proposed schemes achieve good noise suppression up to 20 dB while experiencing very low levels of speech distortion.

Hai Quang Dam

Papers

Deleted DOI: Postfiltering Using Multichannel Spectral Estimation in Multispeaker Environments

Space constrained beamforming with source PSD updates

Adaptive microphone array with noise statistics updates

Source separation employing beamforming and SRP-PHAT localization in three-speaker room environments

Adaptive Microphone Array Employing Spatial Quadratic Soft Constraints and Spectral Shaping