With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing u...
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With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing unit, due to the limited energy and bandwidth resources available to the sensors. In this paper, we propose a distributed independent component analysis (ICA) algorithm, which aims at identifying the original signal sources based on observations of their mixtures measured at various sensor nodes. One of the most commonly used ICA algorithms is known as FastICA, which requires a spatial pre-whitening operation in the first step of the algorithm. Such a pre-whitening across all nodes of a WSN is impossible in a bandwidth-constrained distributed setting as it requires to correlate each channel with each other channel in the WSN. We show that an explicit network-wide pre-whitening step can be circumvented by leveraging the properties of the so-called distributed Adaptive Signal Fusion (DASF) framework. Despite the lack of such a network-wide pre-whitening, we can still obtain the $Q$ least Gaussian independent components of the centralized ICA solution, where $Q$ scales linearly with the required communication load.
A rapid increase in popularity of smart devices equipped with acoustic sensors enables the design of speech enhancement methods for distributed setups. In this work, we present a distributed noise reduction scheme in ...
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ISBN:
(纸本)9781728119465
A rapid increase in popularity of smart devices equipped with acoustic sensors enables the design of speech enhancement methods for distributed setups. In this work, we present a distributed noise reduction scheme in which the time-frequency masks and spatial filters are estimated at the nodes based on the locally available microphone signals and the compressed signals received from other nodes, where each node transmits only a single signal. The proposed block processing facilitates online estimation of masks and distributedspatial filters in an interchanged fashion. The results of performed numerical experiments indicate that the proposed online distributed noise reduction scheme performs similarly to the centralized approach, in which signals of all microphones of the distributed arrays are available for joint processing, and it significantly outperforms the local approach in which only the local microphone signals are available for the estimation of masks and spatial filters.
The distributed adaptive signal fusion (DASF) algorithm is a generic algorithm that can be used to solve various spatial signal and feature fusion optimization problems in a distributed setting such as a wireless sens...
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ISBN:
(纸本)9789082797091
The distributed adaptive signal fusion (DASF) algorithm is a generic algorithm that can be used to solve various spatial signal and feature fusion optimization problems in a distributed setting such as a wireless sensor network. Examples include principal component analysis, adaptive beamforming, and source separation problems. While the DASF algorithm adaptively learns the relevant second order statistics from the collected sensor data, accuracy problems can arise if the spatial covariance structure of the signals is rapidly changing. In this paper, we propose a method to improve the tracking or convergence speed of the DASF algorithm in a fully-connected sensor network with a broadcast communication protocol. While the improved tracking increases communication cost, we demonstrate that this tradeoff is efficient in the sense that an L-fold increase in bandwidth results in an R times faster convergence with R >> L.
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