Distributed classification of acoustic targets in wireless audio-sensor networks

Malhotra, Baljeet; Nikolaidis, Ioanis; Harms, Janelle

doi:10.1016/j.comnet.2008.05.008

Cited by 44 publications

(25 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…a bkn ·ˆ * bkn (15) with α denoting an adaptation factor which determines the weight which is given to the own estimate and the neighborhood estimates, respectively, and a bkn being a weighting factor chosen as…”

Section: Combinationmentioning

confidence: 99%

“…Step: Each sensor j combines its neighbor's estimates analogously to (14) and (15) in order to obtain improved estimatesŵ jkn andˆ jkn .…”

Section: Combinementioning

confidence: 99%

“…In particular, object or speaker labeling can be solved by in-network adaptive classification algorithms where a minimum amount of information is exchanged among single-hop neighbors. Various methods have been proposed that deal with distributed data clustering and classification, e.g., [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In the last few years, several distributed adaptive strategies, such as incremental, consensus, and diffusion least mean squares algorithms have been developed [25].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Robust and adaptive diffusion-based classification in distributed networks

Binder

Muma

Zoubir

2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Distributed adaptive signal processing and communication networking are rapidly advancing research areas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement in adverse environments. An emerging new paradigm is that of multiple devices cooperating in multiple tasks (MDMT). This is different from the classical wireless sensor network (WSN) setup, in which multiple devices perform one single joint task. A crucial first step in order to achieve a benefit, e.g., a better node-specific audio signal enhancement, is the common unique labeling of all relevant sources that are observed by the network. This challenging research question can be addressed by designing adaptive data clustering and classification rules based on a set of noisy unlabeled sensor observations. In this paper, two robust and adaptive distributed hybrid classification algorithms are introduced. They consist of a local clustering phase that uses a small part of the data with a subsequent, fully distributed on-line classification phase. The classification is performed by means of distance-based similarity measures. In order to deal with the presence of outliers, the distances are estimated robustly. An extensive simulation-based performance analysis is provided for the proposed algorithms. The distributed hybrid classification approaches are compared to a benchmark algorithm where the error rates are evaluated in dependence of different WSN parameters. Communication cost and computation time are compared for all algorithms under test. Since both proposed approaches use robust estimators, they are, to a certain degree, insensitive to outliers. Furthermore, they are designed in a way that they are applicable to on-line classification problems.

show abstract

Section: Combinationmentioning

confidence: 99%

“…Step: Each sensor j combines its neighbor's estimates analogously to (14) and (15) in order to obtain improved estimatesŵ jkn andˆ jkn .…”

Section: Combinementioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Robust and adaptive diffusion-based classification in distributed networks

Binder

Muma

Zoubir

2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…• The matrix w r o , connecting the output to the reservoir, is initialized as a zero matrix, as feedback was not 13 found to provide improvement in performance in this case.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Thanks to the presence of recurrent connections, RNNs are able to efficiently capture the dynamics in the underlying process to be learned. Tasks that would benefit from such algorithms abound, including distributed multimedia classification [12], event detection with array of microphones [13], classification of texts in cluster environments [14] and prediction of highly nonlinear time-series in wireless sensor networks [4]. Still, it is known that training an RNN model is a challenging task even in a centralized context, which is far from being fully solved [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Distributed Reservoir Computing with Sparse Readouts [Research Frontier]

Scardapane

Panella

Comminiello

et al. 2016

IEEE Comput. Intell. Mag.

View full text Add to dashboard Cite

In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development of distributed, message-passing implementations of the standard machine learning training algorithms. In this paper, we are concerned with the distributed training of a particular class of recurrent neural networks, namely echo state networks (ESNs). In the centralized case, ESNs have received considerable attention, due to the fact that they can be trained with standard linear regression routines. Based on this observation, in our previous work we have introduced a decentralized algorithm, framed in the distributed optimization field, in order to train an ESN. In this paper, we focus on an additional sparsity property of the output layer of ESNs, allowing for very efficient implementations of the resulting networks. In order to evaluate the proposed algorithm, we test it on two well-known prediction benchmarks, namely the Mackey-Glass chaotic time series and the 10th order nonlinear auto regressive moving average (NARMA) system.

show abstract

In-Network Processing for Multimedia Sensor Networks

Liu

Luo

2021

Multimedia Sensor Networks

View full text Add to dashboard Cite

Distributed classification of acoustic targets in wireless audio-sensor networks

Cited by 44 publications

References 8 publications

Robust and adaptive diffusion-based classification in distributed networks

Robust and adaptive diffusion-based classification in distributed networks

Distributed Reservoir Computing with Sparse Readouts [Research Frontier]

In-Network Processing for Multimedia Sensor Networks

Contact Info

Product

Resources

About