Amin Hassani

Cooperative integrated noise reduction and node-specific direction-of-arrival estimation in a fully connected wireless acoustic sensor network

In this paper, we consider cooperative node-specific direction-of-arrival (DOA) estimation in a fully connected wireless acoustic sensor network (WASN). We consider a scenario where each node is equipped with a local microphone array with a known geometry, but where the position of the nodes, as well as their relative geometry and hence the between-nodes signal coherence model is unknown. The local array geometry in each node defines node-specific DOAs with respect to a set of target speech sources and the aim is to estimate these in each node. We assume a noisy environment with localized and/or diffuse noise sources, i.e., the noise can be correlated over the different microphones. A distributed noise reduction algorithm can then be applied as a preprocessing step to denoise all the microphone signals of the WASN, based on the distributed adaptive node-specific signal estimation (DANSE) algorithm. The denoised local microphone signals can then be used in each node to estimate the node-specific DOAs by using a subspace-based DOA estimation, involving a (generalized) eigenvalue decomposition of the local microphone signal correlation matrices. It is seen that the fused microphone signals that are exchanged between the nodes in the DANSE algorithm can also be included in these correlation matrices to obtain improved DOA estimates, leading to a cooperative integrated noise reduction and DOA estimation scheme, where the noise reduction can actually be shortcut. The improved performance achieved by this cooperative DOA estimation is demonstrated by means of numerical simulations for two different subspace-based DOA estimation methods (MUSIC and ESPRIT). HighlightsNode-specific DOA estimation in a WASN is considered.DOAs are estimated in cooperative integrated noise reduction and DOA estimation scheme.The scheme significantly performs better than the case without cooperation in the WASN.

Multi-Task Wireless Sensor Network for Joint Distributed Node-Specific Signal Enhancement, LCMV Beamforming and DOA Estimation

We consider a multi-task wireless sensor network (WSN) where some of the nodes aim at applying a multi-channel Wiener filter to denoise their local sensor signals, whereas others aim at implementing a linearly constrained minimum variance beamformer to extract node-specific desired signals and cancel interfering signals, and again others aim at estimating the node-specific direction-of-arrival of a set of desired sources. For this multi-task WSN, by relying on distributed signal estimation techniques that incorporate a low-rank approximation of the desired signals correlation matrix, we design a distributed algorithm under which the nodes cooperate with reduced communication resources even though they are solving different signal processing tasks and do not know the tasks of the other nodes. Convergence and optimality results show that the proposed algorithm lets all the nodes achieve the network-wide centralized solution of their node-specific estimation problem. Finally, the algorithm is applied in a wireless acoustic sensor network scenario with multiple speech sources to show the effectiveness of the algorithm and support the theoretical results.

Multi-task wireless acoustic sensor network for node-specific speech enhancement and DOA estimation

We consider the design of a distributed algorithm that is suitable for a wireless acoustic sensor network formed by nodes solving multiple tasks (MDMT). In the network, some of the nodes aim at estimating the node-specific direction-of-arrival of some desired sources. Additionally, there are other nodes that aim at implementing either a multi-channel Wiener filter or a minimum variance distortionless response beamformer in order to estimate node-specific desired signals as they impinge on their microphones. By using compressive filter-and-sum operations that incorporate a low-rank approximation of the sensor signal correlation matrix, the proposed MDMT algorithm let the nodes cooperate to achieve the network-wide centralized solution of their node-specific estimation problems without any knowledge about the tasks of other nodes. Finally, the effectiveness of the algorithm is shown through computer simulations.

LCMV beamforming with subspace projection for multi-speaker speech enhancement

The linearly constrained minimum variance (LCMV) beamformer has been widely employed to extract (a mixture of) multiple desired speech signals from a collection of microphone signals, which are also polluted by other interfering speech signals and noise components. In many practical applications, the LCMV beamformer requires that the subspace corresponding to the desired and interferer signals is either known, or estimated by means of a data-driven procedure, e.g., using a generalized eigenvalue decomposition (GEVD). In practice, however, it often occurs that insufficient relevant samples are available to accurately estimate these subspaces, leading to a beamformer with poor output performance. In this paper we propose a subspace projection-based approach to improve the performance of the LCMV beamformer by exploiting the available data more efficiently. The improved performance achieved by this approach is demonstrated by means of simulation results.

Distributed signal subspace estimation based on local generalized eigenvector matrix inversion

Many array-processing algorithms or applications require the estimation of a target signal subspace, e.g., for source localization or for signal enhancement. In wireless sensor networks, the straightforward estimation of a network-wide signal subspace would require a centralization of all the sensor signals to compute network-wide covariance matrices. In this paper, we present a distributed algorithm for network-wide signal subspace estimation in which such data centralization is avoided. The algorithm relies on a generalized eigenvalue decomposition (GEVD), which allows to estimate a target signal subspace in spatially correlated noise. We show that the network-wide signal subspace can be found from the inversion of the matrices containing the generalized eigenvectors of a pair of reduced-dimension sensor signal covariance matrices at each node. The resulting distributed algorithm reduces the per-node communication and computational cost, while converging to the centralized solution. Numerical simulations reveal a faster convergence speed compared to a previously proposed algorithm.

Tracking of a rotating object in a Wireless Sensor Network using fuzzy based adaptive IMM filter

In this paper, a new fuzzy-logic based adaptive Interactive Multiple Model (IMM) filter is presented for tracking a vehicular rotating object in a Wireless Sensor Network (WSN). In this method, a Fuzzy-logic Inference System (FIS) is employed to adaptively tune the system noise covariance matrix associated with the Nearly Constant Velocity (NCV) model. By reducing the number of interacting models, our algorithm simplifies state-of-the-art IMM algorithms for tracking of a rotating object. Localization for data aggregation process is performed by means of the triangulation method in conjunction with dynamic grouping of sensors. Monte Carlo simulations show that this scheme achieves good tracking performance for both highly rotating and non-rotating objects compared to state-of-the-art IMM algorithms.

Real-time distributed speech enhancement with two collaborating microphone arrays

In this demonstration, we aim at presenting our recent implementation results and provide an evaluation testbed through which users can experiment and compare the outputs of the distributed speech enhancement algorithms in [1–3]. The system allows a user to assess the merits of these algorithms in any acoustic setup. The multi-channel Wiener filter (MWF) is a well-known noise reduction algorithm for multi-microphone speech processing applications. In general, the noise reduction improves as the number of available microphones increases, since a better spatial sampling or diversity can be exploited. Motivated by this, wireless acoustic sensor networks (WASNs), consisting of a multitude of collaborating nodes with an embedded signal processing unit and microphone array, have been proposed to increase the spatial diversity of multi-microphone systems. However, due to the limited per-node computational power and communication bandwidth, reduced-bandwidth distributed processing is more favorable than a centralized processing where all the microphone signals are transmitted to a fusion center. In this demo, we evaluate the so-called distributed adaptive node-specific signal estimation (DANSE) algorithm [1] which is essentially a distributed realization of the MWFs of the individual nodes of a WASN and allows the nodes to cooperate by exchanging pre-filtered and compressed signals, while eventually converging to the same centralized MWF solutions as if each node would have access to all the microphone signals in theWASN [1,2]. In the original version of DANSE in [1], the required speech correlation matrices are estimated using a straightforward subtraction-based method. This method, however, has been shown to deliver an unsatisfying performance in the presence of second-order statistics error (e.g., due to low-SNR conditions, highly non-stationary noise or erroneous voice activity detections (VADs)) [4]. An alternative version of DANSE, called generalized eigenvalue decomposition (GEVD)-based DANSE, has been developed in [3] in which each node incorporates a GEVD-based lowrank approximation of the speech correlation matrix in its local MWF. An in-depth theoretical study of the underlying principals of the GEVD-based DANSE algorithm has been presented in [3]. In order to also evaluate the merits of the GEVD-based DANSE algortihm in a practical realistic environment, a real-time experimental setup has been developed which will be explained in the next section.