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

Abstract

Abstract-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 nodespecific 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).

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

“…Each node k ∈ K MVDR minimizes the output power of its beamformer under a single linear constraint that steers the beam towards the location of the target speech source such that the target speech signal (as received at its reference microphone) is processed without distortion [26]. Finally, each node k ∈ K DOA estimates its node-specific DOA θ k from the target speech source [19]. We assume that the local microphone array geometry of the nodes k ∈ K DOA is known, but the position of these nodes as well as the relative geometry between them and the other nodes are unknown.…”

“…In addition, the noise correlation matrix, defined as Rnn = E{nn H }, is assumed to be either known or to be estimated in the 'noise-only' segments when the target speech source is silent. To distinguish between such segments, a Voice Activity Detection (VAD) is required (as explained in [19,27]). In the sequel, we use an overline to denote a correlation matrix that is estimated from the data, i.e.,R.…”

“…Although this means that we only partially exploit the information, cooperation between nodes has led to an improved GEVD-based estimate ofq (and hence its sub-vectorsq k 's ) [19]. The obtained DOA estimate is denoted asθ k .…”

“…The aforementioned works are all focused on regression-based parameter estimation problems. Other works have focused on other parameter estimation problems such as node-specific direction of arrival (DOA) estimation [19]- [20]. In this setting, each node has a different orientation and hence a different DOA with respect to the target source [19].…”

Indoor Self-Localization and Orientation Estimation of Smartphones Using Acoustic Signals

“…The relative location of the nodes provides us with sufficient spatial information to implement a wireless microphone array (WMA). WMAs have many potential applications in distributed audio processing, such as speech enhancement [4], blind source separation and echo cancelation [5], speaker localization and tracking [6,7], and voice activity detection [8].…”