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Signal processing algortihms for wireless acoustic sensor networks.
Recent academic developments have initiated a paradigm shift in the wayspatial sensor data can be acquired. Traditional localized and regularly arranged sensor arrays are replaced by sensor nodes that are randomly distributed over the entire spatial field, and which communicate with each other or with a master node through wireless communication links. Together, these nodes form a so-called `wireless sensor network' (WSN). Each node of a WSN has a local sensor array and a signal processing unit toperform computations on the acquired data. The advantage of WSNs compared to traditional (wired) sensor arrays, is that many more sensors can be used that physically cover the full spatial field, which typically yields more variety (and thus more information) in the signals. It is likely that future data acquisition, control and physical monitoring, will heavily rely on this type of networks. Most contributions in this thesis focus on (but are not limited to) the application of WSNs for distributednoise reduction in speech recordings. Noise reduction for speech enhancement is crucial in many applications such as hearing aids, mobile phones, video conferencing, hands-free telephony, automatic speech recognition, etc.
In this thesis, we develop novel signal and parameter estimation techniques that rely on distributed in-network processing,i.e., without gathering all the sensor data in a central processor as it is the case in centralized estimation algorithms. In WSNs, a distributed approach is often preferred, especially so when it is scalable in terms of its communication bandwidth requirement, transmission power and local computational complexity. In almost all distributed estimation techniques that are proposed in this thesis, the goal is to obtain the same estimation performance as in a centralized estimation algorithm. We distinguish between two different types of distributed estimation problems: signal estimation and parameter estimation. Both problems usually have tobe tackled in very different ways. In distributed signal estimation, the number of estimation variables grows linearly with the number of temporal observations, i.e. for each sample time of the sensors, a new sampleof the desired signal(s) has to be estimated. Iterative refinement of these signal estimates would require that intermediate signal estimates are retransmitted multiple times between the same node pairs, which is usually not feasible in real-time systems with high sampling rates. In distributed parameter estimation problems on the other hand, the number of estimation variables are either fixed, i.e., it does not grow with the number of temporal observations, or the data acquisition happens at a very low sampling rate such that sufficient time is available to iteratively refine intermediate estimates.
In the context of distributed signal estimation in WSNs, we propose a distributed adaptive node-specificsignal estimation (DANSE) algorithm, which operates in a fully connected WSN. The term `node-specific' refers to the fact that each node estimates adifferent signal, although the desired signals of all nodes have toshare a common low-dimensional signal subspace. In this case, DANSE significantly reduces the exchange of data between nodes, while still obtaining an optimal estimator in each node, as if all nodes have access to all the sensor signal observations in the network. In the original version of DANSE, the local fusion rules of each node are iteratively updated in a sequential round-robin fashion. The DANSE algorithm is then extended to the case where nodes update their local fusion rules simultaneously, which allows the algorithm to adapt more swiftly to changes in the environment. Both versions of the algorithm are then applied in a speech enhancement context. To this end, the algorithm is extended to a more robust version, to avoid numerically ill-conditioned quantities that often arise in such practical settings. The DANSE algorithm is also extended tooperate in WSNs with a tree topology, hence relaxing the constraint that the network has to be fully connected, i.e., each node only has to communicate with nearby nodes. Finally, the DANSE algorithm is extended with node-specific linear constraints, yielding an optimal node-specific linearly-constrained minimum variance beamformer in each node.
In the second part of this thesis, we tackle distributed linear regression problems, based on distributed parameter estimation techniques. In particular, we focus on the case where the data or regression matrix is noisy,for which traditional least-squares methods yield biased results. To reduce this bias, we propose two novel methods. The first one is a distributed version of the well-known total least squares estimation technique,which yields unbiased estimates if the regressor noise is white. A second method, that can also cope with colored noise, is based on a bias-compensated recursive least squares algorithm with diffusion adaptation. This algorithm is analyzed in an adaptive filtering context, where it is demonstrated that the cooperation between nodes indeed reduces the bias, and furthermore reduces the variance of the local parameter estimates ateach node.
In the third part of this thesis, we propose two supporting techniques that can be used in WSNs for (acoustic) signal estimation. The first one is an energy-based multi-speaker voice activity detection algorithm, that aims to track the individual speech power of multiple speakers talking simultaneously. Finally, we propose a technique for sensor subset selection, which is an efficient greedy approach to selectthe subset of sensors that contribute the most to the estimation. The other nodes can then be put to sleep to save energy. This method also yields efficient formulas to compute optimal fall-back estimators in the case of link failure.
In this thesis, we develop novel signal and parameter estimation techniques that rely on distributed in-network processing,i.e., without gathering all the sensor data in a central processor as it is the case in centralized estimation algorithms. In WSNs, a distributed approach is often preferred, especially so when it is scalable in terms of its communication bandwidth requirement, transmission power and local computational complexity. In almost all distributed estimation techniques that are proposed in this thesis, the goal is to obtain the same estimation performance as in a centralized estimation algorithm. We distinguish between two different types of distributed estimation problems: signal estimation and parameter estimation. Both problems usually have tobe tackled in very different ways. In distributed signal estimation, the number of estimation variables grows linearly with the number of temporal observations, i.e. for each sample time of the sensors, a new sampleof the desired signal(s) has to be estimated. Iterative refinement of these signal estimates would require that intermediate signal estimates are retransmitted multiple times between the same node pairs, which is usually not feasible in real-time systems with high sampling rates. In distributed parameter estimation problems on the other hand, the number of estimation variables are either fixed, i.e., it does not grow with the number of temporal observations, or the data acquisition happens at a very low sampling rate such that sufficient time is available to iteratively refine intermediate estimates.
In the context of distributed signal estimation in WSNs, we propose a distributed adaptive node-specificsignal estimation (DANSE) algorithm, which operates in a fully connected WSN. The term `node-specific' refers to the fact that each node estimates adifferent signal, although the desired signals of all nodes have toshare a common low-dimensional signal subspace. In this case, DANSE significantly reduces the exchange of data between nodes, while still obtaining an optimal estimator in each node, as if all nodes have access to all the sensor signal observations in the network. In the original version of DANSE, the local fusion rules of each node are iteratively updated in a sequential round-robin fashion. The DANSE algorithm is then extended to the case where nodes update their local fusion rules simultaneously, which allows the algorithm to adapt more swiftly to changes in the environment. Both versions of the algorithm are then applied in a speech enhancement context. To this end, the algorithm is extended to a more robust version, to avoid numerically ill-conditioned quantities that often arise in such practical settings. The DANSE algorithm is also extended tooperate in WSNs with a tree topology, hence relaxing the constraint that the network has to be fully connected, i.e., each node only has to communicate with nearby nodes. Finally, the DANSE algorithm is extended with node-specific linear constraints, yielding an optimal node-specific linearly-constrained minimum variance beamformer in each node.
In the second part of this thesis, we tackle distributed linear regression problems, based on distributed parameter estimation techniques. In particular, we focus on the case where the data or regression matrix is noisy,for which traditional least-squares methods yield biased results. To reduce this bias, we propose two novel methods. The first one is a distributed version of the well-known total least squares estimation technique,which yields unbiased estimates if the regressor noise is white. A second method, that can also cope with colored noise, is based on a bias-compensated recursive least squares algorithm with diffusion adaptation. This algorithm is analyzed in an adaptive filtering context, where it is demonstrated that the cooperation between nodes indeed reduces the bias, and furthermore reduces the variance of the local parameter estimates ateach node.
In the third part of this thesis, we propose two supporting techniques that can be used in WSNs for (acoustic) signal estimation. The first one is an energy-based multi-speaker voice activity detection algorithm, that aims to track the individual speech power of multiple speakers talking simultaneously. Finally, we propose a technique for sensor subset selection, which is an efficient greedy approach to selectthe subset of sensors that contribute the most to the estimation. The other nodes can then be put to sleep to save energy. This method also yields efficient formulas to compute optimal fall-back estimators in the case of link failure.
Date:1 Jan 2008 → 31 Dec 2011
Keywords:Acoustic sensor networks
Disciplines:Mechanics
Project type:PhD project